ArticlePDF AvailableLiterature Review

Dynamics of cumulative advantage and threats to equity in open science: a scoping review

Article

Dynamics of cumulative advantage and threats to equity in open science: a scoping review

Abstract and Figures

Open Science holds the promise to make scientific endeavours more inclusive, participatory, understandable, accessible and re-usable for large audiences. However, making processes open will not per se drive wide reuse or participation unless also accompanied by the capacity (in terms of knowledge, skills, financial resources, technological readiness and motivation) to do so. These capacities vary considerably across regions, institutions and demographics. Those advantaged by such factors will remain potentially privileged, putting Open Science's agenda of inclusivity at risk of propagating conditions of ‘cumulative advantage’. With this paper, we systematically scope existing research addressing the question: ‘What evidence and discourse exists in the literature about the ways in which dynamics and structures of inequality could persist or be exacerbated in the transition to Open Science, across disciplines, regions and demographics?’ Aiming to synthesize findings, identify gaps in the literature and inform future research and policy, our results identify threats to equity associated with all aspects of Open Science, including Open Access, Open and FAIR Data, Open Methods, Open Evaluation, Citizen Science, as well as its interfaces with society, industry and policy. Key threats include: stratifications of publishing due to the exclusionary nature of the author-pays model of Open Access; potential widening of the digital divide due to the infrastructure-dependent, highly situated nature of open data practices; risks of diminishing qualitative methodologies as ‘reproducibility’ becomes synonymous with quality; new risks of bias and exclusion in means of transparent evaluation; and crucial asymmetries in the Open Science relationships with industry and the public, which privileges the former and fails to fully include the latter.
This content is subject to copyright.
royalsocietypublishing.org/journal/rsos
Review
Cite this article: Ross-Hellauer T, Reichmann S,
Cole NL, Fessl A, Klebel T, Pontika N. 2022
Dynamics of cumulative advantage and threats to
equity in open science: a scoping review. R. Soc.
Open Sci. 9: 211032.
https://doi.org/10.1098/rsos.211032
Received: 14 June 2021
Accepted: 15 December 2021
Subject Category:
Science, society and policy
Subject Areas:
e-science
Keywords:
open science, research policy,
cumulative advantage, equity
Author for correspondence:
Tony Ross-Hellauer
e-mail: tross@know-center.at
Electronic supplementary material is available
online at https://doi.org/10.6084/m9.figshare.c.
5797144.
Dynamics of cumulative
advantage and threats to
equity in open science:
a scoping review
Tony Ross-Hellauer
1,2
, Stefan Reichmann
2
,
Nicki Lisa Cole
1,2
, Angela Fessl
1,2
, Thomas Klebel
1
and
Nancy Pontika
3
1
Know-Center GmbH, Graz, Austria
2
Open and Reproducible Research Group, Graz University of Technology, Inffeldgasse 13,
8010 Graz, Austria
3
The Open University, Milton Keynes, UK
TR-H, 0000-0003-4470-7027; NLC, 0000-0002-6034-533X
Open Science holds the promise to make scientific endeavours
more inclusive, participatory, understandable, accessible and
re-usable for large audiences. However, making processes
open will not per se drive wide reuse or participation unless
also accompanied by the capacity (in terms of knowledge,
skills, financial resources, technological readiness and
motivation) to do so. These capacities vary considerably across
regions, institutions and demographics. Those advantaged by
such factors will remain potentially privileged, putting Open
Sciences agenda of inclusivity at risk of propagating
conditions of cumulative advantage.Withthispaper,we
systematically scope existing research addressing the question:
What evidence and discourse exists in the literature about the
ways in which dynamics and structures of inequality could
persist or be exacerbated in the transition to Open Science,
across disciplines, regions and demographics?Aiming to
synthesize findings, identify gaps in the literature and inform
future research and policy, our results identify threats to
equity associated with all aspects of Open Science, including
Open Access, Open and FAIR Data, Open Methods, Open
Evaluation, Citizen Science, as well as its interfaces with
society, industry and policy. Key threats include: stratifications
of publishing due to the exclusionary nature of the author-
pays model of Open Access; potential widening of the digital
divide due to the infrastructure-dependent, highly situated
nature of open data practices; risks of diminishing qualitative
methodologies as reproducibilitybecomes synonymous with
quality; new risks of bias and exclusion in means of
© 2022 The Authors. Published by the Royal Society under the terms of the Creative
Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits
unrestricted use, provided the original author and source are credited.
transparent evaluation; and crucial asymmetries in the Open Science relationships with industry and
the public, which privileges the former and fails to fully include the latter.
1. Introduction
Academia remains critically inequitable. The Global North dominates authorship and collaborative
research networks, pushing the Global South to the periphery [1,2]. Even within richer regions, a
fetish for the poorly defined goal of excellence[3] breeds cumulative advantage in funding allocation
for the highest-funded institutions [4]. At the level of individuals, early success shapes future success
[5]. Women occupy relatively fewer higher positions, tend to achieve senior positions at a later age,
are awarded less grant funding and have fewer high-impact publications[69]. Lack of equity has
been found to shut out participation in the scientific conversation and potentially reduce motivation,
happiness and willingness to work, even among those who actually benefit [10]. These inequalities
undoubtedly testify to broader societal imbalances but, as observed since the 1960s [11], dynamics of
social mobility play out in academia in specific ways (cf. [12]).
Open Science
1
has been proposed at least in part as a corrective for some of these issues. Open Science
has been defined as transparent and accessible knowledge that is shared and developed through
collaborative networks[13]. It is a varied movement to reform research through more transparent and
participatory practices including Open Access to publications, research data sharing, opening research
methods and processes, new means of transparent research evaluation and the re-orientation of
research to be more inclusive of and responsive to the needs of society and industry [14]. Its
motivations are diverse. Fernández Pinto [15] argues that Open Science can be variously seen, inter
alia, as a culture, a goal, a movement, a set of policies, a project and a research strategy. Fecher &
Friesikes [16] definition of Open Science is as an umbrella term encompassing a multitude of
assumptions. They identify five distinct schools of thoughtreflecting the diverse motivations
underpinning Open Science:
Infrastructure School: Aims to create open platforms, tools and services to enable efficient and
collaborative research.
Public School: Aims to make science accessible to citizens and others beyond academia.
Measurement School: Aims to develop alternative assessment systems for research.
Democratic School: Aims to make knowledge freely available to everyone.
Pragmatic School: Aims to make scientific processes more efficient, collaborative and open.
Social and epistemic justice are central to at least two of these motivations (Democratic and Public
Schools), but important drivers of all. Equity has been a key aim of Open Science since its inception.
The stirring language of the foundational 2002 Budapest Open Access Initiative, for example, claimed
Open Access could share learning between rich and poor and lay the foundation for uniting
humanity in a common intellectual conversation and quest for knowledge[17]. Nielsens seminal
Reinventing Discovery devotes a chapter to the ways in which networked Open Science is
democratizingresearch [18]. More recently, increased equitywas listed as a key success factorfor
Open Science by a stakeholder-driven study [19]. As Grahe et al. [20] say, Open science principles of
openness and transparency provide opportunities to advance diversity, justice and sustainability by
promoting diverse, just and sustainable outcomes.
However, equity is one aim of Open Science among others, including increasing research quality and
efficiency. Depending on definitions and priorities, these overlapping aims may conflict. What is more,
these aims are necessarily refracted through the competing motivations of a myriad of actors (including
researchers, research institutions, funders, governments, publishers). The equivocal nature of Open
Science hence leaves room for interpretative flexibility in adoption and implementation, while its
heavy political and economic implications mean that diverse and potentially conflicting motives are at
play. Disconnects between expressed ideals and eventual policies and practices should be expected.
This is especially so since academia seems perniciously vulnerable to logics of cumulative
advantage, as has been recognized at least since Merton proposed the existence of the Matthew
1
In English, the word scienceis taken to exclude the arts and humanities. Hence the term Open Scienceis often taken to be
exclusionary of these domains, and more inclusive terms like open scholarshipor open researchcan be preferred by some. We
here use the more common term Open Science, but this should be read as referring to research from all academic disciplines.
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
2
effect, whereby already successful scientists tend to receive disproportionately high recognition or
rewards (e.g. reputation, resources, access to infrastructure) in comparison to their less-famous
counterparts [2123]. For Merton, systems of reward, allocation of resources and other elements of
social selection thus operate to create and to maintain a class structure in science by providing a
stratified distribution of chances among scientists for significant scientific work[23]. Subsequent
research identified the Matthew effect at work in research at the level of article citations [24], journals
[25], institutions [26], departments [27] and countries [28], and along persistent fault lines of
inequality like race [29] and gender [30]. It is at work across a range of scientific activities, including
peer review [31], public engagement [32] and funding acquisition [33]. Although for Merton the
Matthew effect was potentially detrimental in clustering resources and stifling innovation, he also saw
it as a functional element aiding assessment of the credibility of sources, allocation of attention and
recognizing outstanding contributions [31]. But while the Matthew effect in its various forms might be
functional at a system level, it no doubt has the effect of advantaging and disadvantaging the
contributions of individuals, as well as the individuals themselves, based on secondary attributes.
Given the equity aim of Open Science, this is problematic per se.
Merton later broadened his thought to identify the Matthew effect as an example of cumulative
advantage, whereby comparative advantages of trained capacity make for successive increments of
structural location, and available resources make for advantage such that the gaps between the haves and
the have-nots in science (as in other domains of social life) widen until dampened by countervailing
processes[23]. The lines delineating the Matthew effect and cumulative advantage are often blurred.
2
For
our purposes, and to avoid confusion, in what follows we will prefer the broader term cumulative
advantage, and define it along with DiPrete & Eirich [38] as a general mechanism for inequality across
any temporal process in which a favourable relative position becomes a resource that produces further
relative gains.Thesemechanismsarealsocloselyrelatedtowhatisreferredtoaspreferentialattachment
in network theory, where power-law distributions are a result of the positionality and individual
attributes of specific agents as nodes in a network shape possibilities for future accrual of resources
within that network, such as larger nodes having more possibility for connection [39].
We hence understand Open Science as a diverse agenda to increase transparency, accessibility and
participation in research, where equity is a commonly stated aim. We also, however, understand that
various aspects of academia are particularly vulnerable to the logics of cumulative advantage.
Bringing these threads together, we are led to ask whether Open Science is itself affected by such
mechanisms, and whether they endanger the equity aim of Open Science.
As argued by Albornoz et al. [40], Open Science policies are situated within power imbalances and
historical inequalities with respect to knowledge production (cf. [41]). Uncritical narratives of
openness, therefore, may fail to address structural barriers in knowledge production and hence
perpetuate the cumulative advantage of dominant groups and the knowledge they produced. Making
processes open requires capacities (in terms of knowledge, skills, financial resources, political will,
technological readiness and motivation) which vary across regions, institutions and demographics. In
addition, persistent structural inequalities and social and cognitive biases will not be eliminated in an
Open Science world. We must, therefore, ask how equitable is the implementation of Open Science
across a range of stakeholder categories, in particular those at the peripheries? Might interventions in
some cases actually deepen inequalities or be at conflict with wider Open Science goals? How do
geographical, socio-economic, cultural and structural conditions lead to peripheral configurations in
the Open Science landscape? What factors are at play and what can be done (at a policy level) to
enhance uptake and contribution to the production of scientific knowledge by everybody?
With this paper, we aim to systematically scope existing research to answer the question: What
evidence and discourse exists in the literature about the ways in which dynamics and structures of
inequality could persist or be exacerbated in the transition to Open Science, across disciplines, regions
and demographics?Our scope includes all aspects of Open Science, including Open Access, Open
Data, FAIR Data, Open Methods, Open Evaluation and Citizen Science, as well as its interactions with
the interfaces between science and society and industry. Results are presented according to these
dimensions. This will synthesize evidence and discourse, identify gaps in the literature and inform
future research and policy. Given that the intention is to describe the general scope of the issues, no
systematic quality appraisal of studies is carried out.
2
Although sociologists may identify it solely as referring to Mertons original context of scholarly reputation and rewards, the Matthew
effect has been taken up to describe phenomena of accumulation in areas as diverse as online markets [34], reading and literacy [35],
sexual networks [36] and transitions to democracy [37].
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
3
This study uses the PRISMA framework [42] to align study selection with the research question and
will follow the relevant aspects of the PRISMA Extension for Scoping Reviews to ensure thorough
mapping, reporting and analysis of the literature. As Tricco et al. state, scoping reviews are useful to
examine the extent (that is, size), range (variety) and nature (characteristics) of the evidence on a
topic or question; determine the value of undertaking a systematic review; summarize findings from a
body of knowledge that is heterogeneous in methods or discipline; or identify gaps in the literature to
aid the planning and commissioning of future research.
Since the many potential benefits of Open Science have been well-argued elsewhere [4346], our
presentation here necessarily focuses in greater depth on those areas where Open Science
implementation potentially endangers the aim of greater equity in science. This emphasis should not
be interpreted as signalling that the authors believe that the negatives outweigh the positives. Yet
Open Science has now undoubtedly come of age, as mainstream policy in many regions and
institutions, and must itself be open to critical and continued reflection upon the ways in which
implementation may run counter to ideals. We believe such critique should be welcomedabove all
by Open Science advocatesin order to re-orient implementation strategies and optimize outcomes
wherever possible and desirable.
2. Methods
Methodologically, following identification of the above research question, the work has been structured
according to the following four steps: identify relevant studies, select eligible studies, chart the data,
collate and summarize the results.
2.1. Identifying relevant studies
A search was conducted for published and grey literature on the research area from January 2000 to the
present, published in English. The authors first conducted a search of electronic databases (Scopus and
Web of Science) on 23 December 2020 for citations and literature using the queries detailed below.
Web of Science (All Databases)1627 results
TOPIC: ((open scienceOR science 2.0OR Open AccessOR open peer reviewOR altmetricOR alternative metricOR
open dataOR reproducibOR FAIR DataOR open innovationOR citizen science) AND (matthew effectOR cumulative
advantageOR inequOR justice))
Timespan: 20002020. Databases: WOS, BCI, BIOSIS, CCC, DIIDW, KJD, MEDLINE, RSCI, SCIELO
Search language = English
Scopus1543 results
TITLE-ABS-KEY ((open scienceOR science 2.0OR Open AccessOR open peer reviewOR altmetricOR alternative
metricOR open dataOR reproducibOR FAIR DataOR open innovationOR citizen science) AND (matthew effect
OR cumulative advantageOR inequ)) PUBYEAR > 1999 AND (LIMIT-TO (LANGUAGE, English))
2.2. Selecting eligible studies
Searches yielded 3170 total results. Following manual deduplication, 2661 results remained for title/
abstract screening, which was guided by the PRISMA framework, with specific eligibility criteria
applied to ensure relevance for the study and its research questions. The selection process followed
the recommendations in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses
Extension for Scoping Reviews (PRISMA-ScR) checklist and mapped using the PRISMA-P chart
(figure 1). Web search engines and other sources were used to identify strongly relevant grey
literature from bodies likely to have produced relevant grey literature reports such as research
funders, research-performing organizations, academic publishers, student coalitions, OECD and UN.
Finally, this was augmented by hand-searching references of the included studies and references
(snow-balling). The following inclusion criteria were applied:
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
4
Articles on potential effects in Open Science as they relate to the propagation of cumulative advantage.
Conducted internationally or nationally.
Published from 1 January 2000 until current.
Available in English.
Full-text could be obtained.
Study is a review article, commentary article, editorial, conference paper or other peer-reviewed article.
Study is a grey-literature report from a recognized stakeholder.
All types of methodology (quantitative, qualitative, mixed, etc.) are eligible.
Based on these criteria, two reviewers (first and second authors of this paper) then separately assessed
eligibility via screening of titles and abstracts. Where at least one reviewer perceived the study eligible, it
was included (50% necessary percentage agreement). In total, 239 articles were judged relevant by at least
one reviewer. Full texts were retrieved on 2 February 2021. All reasonable attempts were made to obtain
full-text copies of selected articles (if not openly accessible, then first via institutional access privileges,
and if that failed via inter-library loans or contacting the authors directly), whereupon a further 31
articles were removed as their full text was in a language other than English or the full text could not
be obtained. Following this, 208 articles were carried forward to the next stage.
Full texts of the remaining articles were then examined by the first and second authors to determine
to which research sub-questions the article was relevant. This literature was then delegated among
authors according to topic.
3
2.3. Charting the data and summarizing results
Each author responsible for that theme then appraised the full text to determine whether the study
contained relevant evidence or discourse. Where it did (n= 105), a data charting form (table 1) was
followed to electronically capture relevant information from each included study.
3170 records identified from:
identification of studies via databases and registers
identificationscreening
included
identification of studies
via other methods
Scopus (n = 1543)
Web of Science (n = 1627) duplicate records removed
(n = 509)
records removed before
screening:
records screened
(n = 2661)
reports sought for retrieval
(n = 239)
records excluded
(n = 2422)
reports not retrieved (full-text
not available or not in English)
(n = 31)
reports excluded (not relevant
according to the inclusion criteria
or no relevant findings): 103
reports assessed for eligibility
(n = 208)
studies included in review
(n = 105)
web searching
organizations
citation searching
(n = 163)
records identified
from:
Figure 1. PRISMA diagram showing the literature searching and scoping process. Adapted from [42]. For more information, visit:
http://www.prisma-statement.org/.
3
T.R.-H.: general factors, open evaluation; S.R.: Open/FAIR Data, policy aspects; N.P.: Open Access; T.K.: Open Methods; N.L.C.:
society aspects; A.F.: industry aspects.
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
5
Authors then used further snow-balling and specific keyword search using web search engines
and other sources to identify further relevant peer-reviewed material as well as grey literature,
yielding a total of 163 items identified by other methods, in addition to the 105 items identified via
Scopus, for inclusion. All results were then exported to a single library in the Zotero open source
reference management software. Data charting was collated in a combined CSV file. The co-author
responsible for each theme then drafted an initial narrative summary of the evidence. These sections
were then compiled by the lead author and revised into a full first draft, which was then shared with
all co-authors, who worked collaboratively to revise the study and fill any perceived gaps in evidence
and argument.
These methods were pre-registered in advance on 22 December 2020 (https://osf.io/t6uy9/). All
materials and data are available at doi:10.5281/zenodo.4936202. This resulting paper deviates slightly
from the pre-registration in broadening the title and framing of the paper from a narrow focus on the
Matthew effect to dynamics of cumulative advantage and threats to equity more broadly, in order to
better reflect the scope of the pre-registered search queries and the resultant paper.
3. Results
The following sections present our synthesis of this literature. Since the many potential benefits of Open
Science have been well-argued elsewhere [4346], our presentation here necessarily focuses in greater
depth on those areas where Open Science implementation potentially endangers the aim of greater
equity in science. This emphasis should not be interpreted as signalling that the authors believe that
the negatives outweigh the positives. The presentation of the results is in a descriptive format
(narrative summary) to align with the study objectives and scope of the review, and phenomenon-
oriented according to the various dimensions of Open Science: Open Access, Open Data, FAIR Data,
Open Methods and Open Infrastructure, Open Evaluation, as well as Open Sciences interfaces with
society, industry and policy. It begins with some overarching issues which apply generally across the
dimensions of Open Science.
3.1. Overarching issues concerning inequity in Open Science
Open Science is aimed in part to counteract inequity. Opening access to publications enables readership
beyond those privileged by journal subscriptions [47,48]. Data sharing and open methods allow reuse
beyond the narrow networks of existing collaboration [49]. Greater transparency in processes of
evaluation might eliminate bias in selection procedures [50,51]. Participatory processes of Citizen
Science could make scientific endeavours more inclusive and understandable for large audiences [18].
Table 1. Data charting form.
data chart heading description
author name of author(s)
date date article sourced
title of study title of the article or study
publication year year that the article was published
publication type journal, website, conference, etc.
DOI/URL unique identier
relevance to which study questions Open Access, Open/FAIR Data, Open Methods, Open Evaluation,
society, industry, policy
key ndings, including study aims, details,
design and data sources (where relevant)
noteworthy results of the study that contribute to the scoping review
question(s). Where relevant, overview of the main objectives of the
study. Type of study, empirical or review, etc. Notes on methods
used in the study (whether qualitative or quantitative, which
population demographics studied, etc.). Detail the data sources
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
6
But, as Chin et al. [52] remind us, transitioning to open research involves significant financial costs.
Open Science relies upon local training and support, as well as infrastructure and resources. Even in well-
resourced regions such as Europe [53,54] and the USA [55], readiness-levels of training and support
infrastructure among nations and institutions are highly diverse. These disparities are, of course, even
greater in what Siriwardhana [56] terms resource-poorsettings. Given that Open Science practices
depend on underlying digital competences [57], the continuing realities of the digital divide [58] have
real effects on participation in an Open Science world.
Implementation of Open Science must also be supported by policy. As Prainsack & Leonelli [59] argue,
open science is a political project to an even greater extent than it is a technological one. In Europe, the
Open Science policy landscape is highly variable across nations, funding organizations and institutions
[60]. Policy priorities shape incentive structures and resource allocation, and hence drive different
implementation strategies. Open Science perhaps began as a grassroots movement of scholars, but its
quick uptake into national and institutional policy has seen it linked to wider goals, including economic
growth. In Europe, the European Commission (EC) has been a driver of Open Science [61]. But as an
influential 2016 EC publication makes explicit, this interest is at least partly motivated by Open
Sciences perceived potential to maintain and promote Europescompetitive edge in global knowledge
markets in the information age[62]. To which we must naturally ask, at whose expense?
In the light of this, we should take seriously a strand of critique which links Open Science to broader
trends to reshape the academy under neoliberal principles to emphasize market principles of competition,
foregrounding its economic role in training the workforce and fostering new products and services, at the
expense of the social mission to provide upward mobility for marginalized populations [6366]. For some
critics, Open Science has the potential to merely fuel these developments. In the words of Tyfield [67], Open
Sciences legacy may be defined by its effects on the construction of a new moral economy of knowledge
production, meaning the marketization of science for the benefit of corporations [67]. Similarly, for
Mirowski, Open Science will result in a platformizationof sciencefor-profit firms colonizing the
research landscape with a host of tools, seeking to construct the One Platform to Rule Them All,and
the research process being subject to increasing division of labour wherein smaller and smaller chunks
are made objects of public scrutiny (e.g. open projects, open laboratory notebooks) [41]. Such
developments could see, in the words of Kansa [68], the cause of opennesssubverted to further
entrench damaging institutional structures and ideologies.
3.2. Inequities in Open Access
The rationale for Open Access (henceforth OA, whereby scientific publications are distributed online, free of
cost or other access barriers under open licensing conditions) to research publications is often centred around
the democratization of knowledgewhat Fitzpatrick [69] calls the ethical desireto remedy an imbalance
between information haves and have nots(cf. [47,48]). OA is posited as boosting return on investment
[70] and as a solution to inequity to informationaccessinregions[7176] and disciplines, especially to
improve public participation in conversations related to social challenges like health, education and
agriculture [7782]. Yet, similar to Open Science more broadly, OA is also not a movement with a
coherent ideological basis[83]. Democratization is but one aim among others, including efficiency gains
through speeding dissemination and potentially lowering publishing costs [47]. The diverse ethical,
political and economic priorities motivating these aims, in turn, present a range of possible routes to OA
implementation. A crucial issue in this regard has been the extent to which policies favour publishing in
OA journals (Gold OA) over author self-archiving of non-OA publications in OA repositories (Green OA).
Gold OA can be supported via a multitude of business models, including consortial funding (also
called Diamond OA; cf. [84]) or volunteer labour [83], but many OA journals and publications are
financed via article processing charges (APCs). The APC model is controversial since the benefit of
OA (free readership) is offset by a new barrier to authorship at the other end of the publication
pipeline. In this regard, the extent to which OA policy has been driven by richer, Global North
nations risks reshaping scholarly communications to enable access but still foster exclusion. As the
costs of APCs are usually borne by institutions or research funders (via project funding), those with
fewer resources are disadvantaged [56,85]. The UK governments 2012 decision to clearly favour
publication in open access or hybrid journals, funded by APCs, as the main vehicle for the
publication of research[86] can be seen as a watershed moment for APC Gold OA. More recently, the
related funder-led initiatives OA2020 and Plan S
4
were initially accused of ignoring experiences and
4
https://www.coalition-s.org/.
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
7
interests of developing nations and lacking support for the advancement of non-commercial open-access
initiatives[87]. Although Plan S has arguably somewhat corrected course here [88,89], the overall impact
of Plan S remains to be seen.
APC-based OA hence risks stratifications of publishing as well-resourced researchers can cover even
the highest APCs while less well-resourced researchers cannot [90100]. Even in well-resourced areas like
the UK, rising costs [101] of APCs is recognized as an issue which will mitigate OAs net benefits [102].
Although many publishers offer fee-waivers and discounts to authors unable to pay, restrictive terms and
administrative burden, as well as the extent to which they seemingly place authors in the position of
asking for charity, mean that they are often criticized as a weak response to an urgent issue
[103,104]. This may have effects on specific demographics. For instance, Niles et al. [105] found that
women tend to take cost into consideration significantly more than do men when deciding where to
publish. This issue is made especially pressing by the citation advantages linked to OA publishing
[46,106]. Usually seen as an important driver for motivating OA, in the light of equity such an
advantage may in fact merely further fuel a classic Matthew effect, privileging those actors with the
resources to pay for OA in the most prominent journals.
Given the problematized [107] but persistent association between journal prestige (often quantified
via the journal impact factor) and publication quality, it is especially problematic, then, that publishers
often charge more for high-impact journal publications [95,99].
5
In addition, APC rates are highly
variable across disciplines [109] and regions [90], and are especially problematic in what Eve has
referred to as the dry climateof funding in the social sciences and humanities [110].
The APC model, combined with the pressure to publish or perish[111], has also helped give rise to
what is termed predatoryjournals and publishers, who collect APCs for publishing articles with little or
no editorial rigour [112]. The extent of the predatory publishingproblem has been argued as overhyped
by traditional publishers eager to discredit OA [113]. Indeed, according to Shen & Björk [114], it is
highly containedto a few countries. Yet even if limited, there is a problem, nonetheless. Authors from
developing nations or with less-developed competences in English, already known to be disadvantaged
in traditional publishing [93,115118] as well as early career researchers with limited publishing
experience, are known to be especially affected by predatory publishing [119124]. Given the stigma
attached to publishing in these venues, predatory publishing, therefore, poses a risk to the development
of early career and developing-world researchers and potentially contributing to what Collyer terms two
separate publishing circuits, leading knowledge produced in the Global South to be systematically
marginalized, dismissed, under-valued or simply not made accessible to other researchers”’ [125].
Such stratifications in publishing, favouring traditionally advantaged actors (including for-profit
publishers), will only exacerbate historical inequalities [126] and undermine wider aims of Open
Science. Hence, as Nyamnjoh argues, for open access to be meaningful questions of content and
the epistemological, conceptual, methodological and contextual specificities that determine or impinge
upon it are crucial[127]. We, therefore, agree with Czerniewicz [128] who argues that such
consequences are the result of too narrow a focus on achieving OA per se, by whichever means,
without acknowledging the inequitable global power dynamics of global knowledge production and
exchange. Rather, she suggests, we must broaden our focus from access to knowledge to full
participation in knowledge creation and in scholarly communication.
3.3. Inequities in Open Data and FAIR Data
Data sharing has been linked to increased citation rates [129], economic growth [46], transparency [130],
reproducibility [131], improved research quality [132], reuse [133] and efficiency [134]. These benefits
have often been dissociated from specific research contexts, however. Hence, some suggest that the
Open Data movement has overestimated the homogeneity of research environments [135], resulting in
a generalized assumption that all scientists will benefit from releasing data, no matter where they are
based[136]. Such an assumption fails to appreciate that conditions for making data available differ
across disciplines [137] and regions [136]. The concept of data as decontextualized facts removable
from the context that underpins the idea of data sharing has come under criticism of late as scholars
are beginning to appreciate that data are situated and mutable [138]. In fact, it has been suggested
[139] that datais an umbrella term whose meaning changes with context: with the specificity of the
research purpose, with the scope of data collection and with the goal of the research [139].
5
Nature even recently agreed terms to charge equivalent APCs of up to 9500, for example [108].
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
8
Data sharing occurs for many reasons, including reproducibility of published research, enabling
others to ask new questions and making publicly funded research publicly available [134,139]. The
situated nature of research practices means motivations vary across research contexts [140]. Hence
data sharing is viewed more favourably in some fields than others [138]. To date, much work on data
practices has arguably been led by the (biomedical) sciences [141], with less attention to other
disciplines. For example, the FAIR principles (to make data Findable, Accessible, Interoperable and
Reusable, but importantly not open) [49] were heavily inspired by life sciences research. This initial
focus has carried over to the scope of empirical work on their adoption [142]. But contexts differ
depending on issues like the readiness-level of data formats [143] and whether the research involves
human subjects [144146]. For these disciplinary reasons, a blanket appreciation of Open Data as
inherently democratic is problematic [147,148]. One-size-fits-all policies may, therefore, disadvantage
those disciplines and actors less able to participate, and further add to the prioritization of
STEM subjects.
Data inequalities are also cumulative, shaped by individual and community characteristics, access to
infrastructure, and political and economic factors [149], affecting the abilities of different groups to
partake in the gift economy[150] of academia. As ethnographies of (non-Western) research [142]
show, access is not enough to guarantee that Open Data can be reused effectively because reuse
requires not only access, but other resources such as skills, money and computing power [148]. Those
working in environments where these are in short supply might be put at a disadvantage
[136,142,151]. Additionally, making use of Open Data is closely linked to data literacy, potentially
marginalizing those that cannot engage with data effectively [152154]. Edelenbos et al. [155] argue
that Open Data are particularly accessible to research institutes with more budget. Hence, increasing
evidence suggests that instead of levelling the playing field, Open Data might simply empower those
already advantaged [149,156,157]. In this way, existing inequalities moderate the positive effects of
Open Data which means they might be just a further mechanism whereby the rich get richer instead
of leading to the democratization of knowledge.
In addition, the effects of data-intensive research on careers should be monitored for their outcomes
regarding equity. Studies of authorship contributions to publications have found a clear gender divide
[158]. Women are more likely to contribute to the investigation, data curation or writing of the
original draft, whereas men are more likely to contribute to tasks associated with seniority
(supervision, funding acquisition, resources). This division of labour and capital among researchers
might reinforce existing hierarchies and cumulative advantages, in that additional workload involved
with Open Data is frequently proportionally carried more by women.
These barriers to participation in Open Data are made ever more pressing by the citation advantages
linked to data sharing [129]. Piwowar et al.s results approximate the original conception of the Matthew
effect through establishing a clear (not necessarily causal) connection between Open Data practices and
citation advantage. Giving due attention to the various contexts within which data sharing does or does
not happen is, therefore, paramount for meeting the goals of inclusivity and openness espoused by the
Open Science agenda.
3.4. Inequities in open methods and open infrastructures
Open Science and in particular open methods, which involve practices like sharing analysis code,
laboratory notebooks or preregistering analyses, hold the promise to counter current concerns
regarding integrity in reporting and the reproducibility of research [44,159,160]. There are a few
potential impacts on equity, however. First, it seems plausible to expect that better-resourced
academics could be early adopters in terms of open methods [92]. Well-resourced institutions can
provide the necessary setting to successfully integrate open practices into the research workflow more
easily. Well-resourced and high-status actors tend to be early adopters in general [161] and methods
such as pre-registration or sharing of research notes and code need additional training, effort and
access to infrastructure to be implemented correctly. To the extent that transparency in research is
increasingly becoming a benchmark for quality [162], these well-resourced players will potentially
have an advantage.
Secondly, the meanings and limits of openness are not uniform across disciplines. Calls to increase the
reproducibility of research findings originated in specific fields, most prominently biomedicine and
psychology [163,164]. While normative calls for increased standards in reporting are diffusing to
further disciplines, it must be recognized that the notion of reproducibility is not equally applicable
everywhere. Methodological approaches as found in mathematics, information sciences and computer
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
9
sciences are clearly better suited for reproduction, based on their reliance on statistics and their level of
control over the research environment. On the other hand, qualitative approaches as found in parts of the
humanities, history and social sciences are more difficult to assess in terms of reproducibility [162,165]. In
the same way that the FAIR data principles have been designed primarily for quantitative data (data that
does not rely on human subjects), extending the standards of quantitative methodologies to qualitative
approaches in attempts to make them more scientificmay obscure unavoidable interpretive work[166]
or further devalue qualitative approaches which cannot meet such criteria. This could then also
reproduce existing inequalities of race and gender, since quantitative-focused fields, in particular
STEM, are known to favour white men, and academic participation among women and racial and
ethnic minorities is higher within the humanities and social sciences [167].
Finally, open methods are heavily infrastructure-dependent, reliant on networked online platforms
and other e-Infrastructures. Concerns have been raised about the extent to which privately owned
platforms may frustrate the aims of Open Science [41,168,169]. Recent years have seen major
publishing corporations like Elsevier, Wiley and Springer (via subsidiary Digital Science) rush to
capture researcher workflows through a host of proprietary tools which often eschew interoperability
in favour of intraoperability with their own product suites [168,170]. Hence, there is a growing
recognition of the need for Open Science infrastructures to themselves be open source and
community-governed to ensure they (and the data they generate) remain community resources
responsive to community needs [168,171174]. As Hall has noted, the Open movement is in danger
of being outflanked, if not rendered irrelevant, as a result of our media environment changing from
being content-driven to being increasingly data-driven. For the data-driven world is one in which the
data centre dominates[175].
Yet sustainability and governance models for open infrastructures remain unclear. Funding often
comes from competitive project grants whose bureaucratic funding logic often requires inflexible and
shorter-term project work-plans be satisfied at the expense of longer-term planning, agile
development and broader interoperability within the infrastructure ecosystem [176]. In addition, open
infrastructures often rely on voluntary contributions from open source communities [177]. In this
regard, we must first ask: who is building and for whom? According to Ehls [178], open-source
communities generally skew heavily young (average age 2732) and male (9198%). How this
homogeneity of contributors may influence issues like gender-bias [179] in design of open source tools
should be monitored. In addition, that mainly younger people may be contributing requires us to
examine the ways in which contributions are rewarded. In the prestige economy of academia, open
source contributions are heavily undervalued in promotion, review and tenure procedures, where
publications still dominate [180]. Hence, we might say that open infrastructures are very often reliant
upon the unpaid contributions of early career researchers, whose precarious employment conditions
[181] mean that their time could be better invested in terms of career advancement [182]. Appropriate
credit and recognition (e.g. during evaluations for hiring, promotion and review) of work involving
open methods and open infrastructures are, therefore, key for attaining equitable outcomes in the
uptake of open methods and development of infrastructures.
3.5. Inequities in open evaluation
Open evaluation identifies the ways in which Open Science principles of transparency and inclusivity can
be applied to the evaluation of research and researchers via peer review or metrics.
Peer review, assessment of research outputs by external experts, is the gold standard for evaluation
and selection in scholarly publishing, conferences and funding allocation, but is often criticized as
inefficient, unreliable and subject to bias [183]. Open peer review applies Open Science to reform peer
review in various ways, most prominently by removing reviewer anonymity or publishing review
reports [51]. A major supposed advantage is increased review quality. Yet opponents counter that this
may compromise review processes, especially considering power imbalances, either by discouraging
full and forthright opinion or opening especially early career reviewers to potential future reprisals
from aggrieved authors later on [184]. Given that a recent study found that publishing reports does
not compromise review quality, at least when allowing anonymity [185], it seems the issue of de-
anonymizing reviewers is the main issue. By contrast to other elements of open peer review, opening
reviewer identities is not favoured by researchers [186].
Research metrics are used throughout research and researcher evaluation processes, usually based
heavily on counting citations, often aggregated via mechanisms like the h-index or Journal Impact
Factor. However, citations have been widely criticized for being too narrow a measure of research
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
10
quality [50,187,188]. The application of particularistic standards is especially perilous for early career
researchers who have yet to build their profile. By using citation metrics to evaluate research
contributions, the Matthew effect leads to the self-reinforcement of initial positive feedback [24].
Moreover, indicators such as the impact factor are highly reactive [189] and therefore exacerbate a
quasi-monopolization of resources (prestige, recognition, money) in the hands of relatively few
institutions and individual researchers.
The rise of the socialweb in the mid-2000s soon gave rise to calls for alternative metricsor
altmetricsto be part of balanced research assessment by aggregating additional online impact
measures such as tweets, likes, shares, bookmarks, blogs and press coverage [190,191]. With their
intention to expand the scope of research assessment using new sources of web data, altmetrics have
been associated with the move to Open Science [192,193]. Perceived advantages include the speed of
data availability and ability to track outputs beyond publications [190,191].
Given general agreement on the limitations of, and over-reliance on, citations, broadening the range
of possible data sources for research evaluation is welcome. Yet altmetrics themselves have been
criticized for a lack of robustness and susceptibility to gaming; disparities of social media use
between disciplines and geographical regions; reliance on commercial entities for the underlying data;
indicating buzzor controversy rather than quality; underrepresentation of data from languages
outside English; and underrepresentation of older papers [183,192,194199]. Each of these could have
consequences for altmetricsefficacy as tools of equitable research assessment and advantage some at
the expense of others. In addition, the very idea of expanding the number of metrics used for the
assessment can be critiqued as merely perpetuating the tyranny of metrics. Ryan [200], for example,
argues that metrics per se are tools of surveillance, enclosing academic freedoms by furthering a
neoliberal idea of the academic as a competitor in a game of visibility, and promulgating a negative
culture of competition that is the root of much ill in the academy (cf. [41,67]).
3.6. Inequities in Citizen Science
Strengthening the relationship between science and society is a key pillar of Open Science. Citizen
Science seeks to foster inclusion in knowledge production by involving the public in scientific
processes. Practices range from research where the public contribute data to scientific projects via
crowdsourcing platforms, to extreme citizen scienceor strongly participatory science, wherein
members of the public participate in all aspects of research and are able to make valuable use of the
research results in ways that benefits their lives and communities [201,202]. It is the latter, rooted in
traditions of participatory research, that most directly and strongly challenges the dynamics of
inequality within academia [201,203]. Participatory research is directed by a self-reflexive, critical,
ethics-focused approach to research design, conduct, distribution of labour [204] and financial benefits
[204206], outputs and impacts [205209]. Questions of equality, justice and equity are often centred
[202,206,208,210212] and the knowledge produced reflects the lived experience and situated expertise
of the participants and communities upon which the research is focused [202,203,213,214]. Further,
those that participate in Citizen Science and participatory projects develop scientific expertise that
would otherwise be unavailable to them, making them more effective democratic actors who are able
to challenge policies, civic expertise and corporate power in pursuit of justice and equality
[202,208,211,215219].
Nevertheless, Citizen Science may also in some circumstances perpetuate inequalities. When
participants serve merely as free data collectors, those who primarily benefit are researchers [209,220
222]. Where such practices bridge the Global North and South, data extraction absent anything else
echoes colonial exploitation [205]. Additionally, there are issues of biased inclusion in terms of the
populations that are invited to participate in traditional Citizen Science [223,224], with the most
marginalized groups likely to be left out [220,221,225,226]. Likewise, there is biased participation in the
crowdsourcing of information [227]. Finally, approaches taken by well-intentioned researchers may also
reinforce existing inequalities, like when a paternalistic stance is taken towards participants (see the use
of provide voicein Hendricks et al. [211]); research seeks to prove the quality and validity of citizen-
generated data, thereby reifying the expertise divide between scientists and the public [210,220,221]; or
when science education is framed as a unidirectional resource coming strictly from scientists (e.g. [228]).
All these have implications for equity in Citizen Science relating back to our themes of how cumulative
advantage can impact participation in Open Science, especially related to issues of digital divide,
differential levels of resources and skills and data equity. Again, it is non-dominant actors who are at
risk. Shelley-Egan et al. [229] criticize such instumentalization of participants as demonstrating that in
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
11
Open Science publics operate as citizen scientists collecting or systematizing data without necessarily
reflecting on or critiquing the broader institutional and societal frameworks for uptake. In summary,
these findings point towards a biased inclusion of populations invited to participate in Citizen Science
projects, which tends to perpetuate the divide between experts and publics and raises questions of
representation and equity: Whose voices are represented in Citizen Science projects, and for which reasons?
3.7. Inequities at the interfaces of Open Science and society, industry and policy
The societal impact of research has been an increasing factor in policymaking throughout academia in
recent years, especially in research evaluation exercises. The drive for Open Science and new forms of
knowledge production has been intimately linked, at least at the policy level, with this agenda [40].
For instance, the EU believes Open Science instrumental in making science more responsive to
societal and economic expectations[62]. However, we can question the terms on which such
responsiveness has thus far been sought. As we shall see, there are crucial asymmetries which
potentially compromise, or at least restrict, Open Sciences potential to fully realize equity in research.
Firstly, we must point out that achieving impact is a resourceful activity. A dominant theme in our
study so far is that enabling access is not enough and this is also the case for societal impact. In
policy uptake of scientific knowledge, for example, it has been suggested that Open Science will help
by making scientific resources more readily available to policy-makers and other policy actors
[46,230,231]. However, such uncritical narratives of openness fail to address structural barriers in
knowledge production. Firstly, more high-profile OA output from established actors may lead to
further over-representation of knowledge produced by dominant groups [140,232,233]. Perhaps more
importantly, though, in addition to access, relationships between academics and policy-makers are a
main factor in policy uptake of science [234]. Policy-makers, with limited time to make decisions and
to seek advice, heuristically rely on (personal) networks of experts that have previously contributed to
policymaking [235]. Researchersand intermediariestranslation skills (elevator test) are particularly
important in this regard [236], with tailored messages a key driver of uptake [237]. Building
relationships and fine-tuning messaging require time and effort, and can be significantly bolstered by
support structures within research-performing institutions [238]. Hence, researchers with access to
such support are advantaged in ensuring uptake.
Knowledge-transfer services are also vital in fostering the uptake of scientific resources in
the industry. Here, indicative evidence shows that Open Science might have a positive economic
impact. A recent synthesis [45] summarizes the literature to find that Open Science can help industry-
uptake through (i) efficiency gains through easier access to publications [239242] and data [243245],
as well as reduction of transaction costs via collaborative approaches [246,247] and lower labour
costs or increasing productivity [240,248250], and (ii) enabling new products, services or collaborative
possibilities [251253]. However, evidence points towards firms (particularly small and medium-
sized enterprises) lacking necessary skills such as information literacy to fully benefit from open
resources [240,254,255]. Given the above discussion of the underlying digital competences necessary
for uptake of FAIR Data, this will be especially acute for those on the wrong side of the digital
divide. Again, the most well-resourced stand to benefit most. Ironically, this fact extends even to the
demonstration of impact. As Bornmann [183] points out, institutional societal impact is often assessed
based on case studies which are expensive and time-consuming to prepare. This all suggests that
uptake of scientific knowledge, even in an age of Open Science, remains prone to dynamics of
cumulative advantage.
There are more fundamental dimensions of asymmetry. Regarding industry, Fernández Pinto [15]
argues that current Open Science policies risk perpetuating the commercialization of science in three
ways. Firstly, the focus on opening publicly funded research allows industry the privileged position of
adopting openness as they see fit, adopting openness where it is commercially attractive or improves
public image (cf. [256]) and ignoring it in less favourable circumstances, such as where findings may
impact sales. Secondly, the policy focus on Open Sciences potential to spur innovation means that the
scienceindustry connection is deepened without critical reflection on the epistemic and social justice
challengesof private sphere research, including scandals in corporate-sponsored scientific research (cf.
[257259]), conflicts of interest and their influence on research work (cf. [260]) and the ways in which
strong intellectual property regimes might inhibit or corrupt scientific research (cf. [261]). Finally,
repeating the point made above, the networked and platform-dependent nature of Open Science
enables commercial interests to increasingly control and commodify research processes [41] (cf. [67]).
For Fernández Pinto, therefore, the issue lies in the unequal terms on which Open Science engages
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
12
industry, with the latter privileged. This asymmetry in favour of industry can in turn can be seen as an
endorsement within Open Science of the marketization of science and the specific neoliberal vision of the
academy which underlies it (cf. [214]).
Shelley-Egan et al. [229] identify another asymmetry, this time at the expense of the public. The authors
compare the Open Science agenda with that of the seemingly complementary responsible research and
innovation (RRI). RRI is a science policy movement, especially prominent in Europe, which seeks to
better integrate science with society [262,263]. Deriving from a tradition of participatory research,
technology assessment and anticipatory governance, RRI aims to avoid expert hubrisand unintended
consequences. Shelley-Egan et al. argue that in contrast to RRI, Open Sciences ambitions are more
pragmatically focused in terms of engagement with the public. Whereas RRIs approach to opening up
extends to an invitation to publics to co-define the aims and means of technical processes in order to
increase their alignment with public values,Open Science restricts ambitions for opening up to
adjustments and improvements to processes based on quality criteria ultimately rooted in the existing
research system. Open Science is thus seen as insufficiently critical of the value and direction of science.
It is also seen as failing to fully appreciate societal voices and citizens as legitimate conversation
partners and beneficiaries of technology and knowledge, engaging publics on asymmetrical terms that
seek mere dialogue between technical experts and societal voices.Hence,RRI focuses more on
producing (ethically and societally) goodoutcomes than on resulting in the (epistemically and
functionally) bestoutcomes, while OS for its part remains agnostic about the former and concerns
itself almost entirely with the latter, and more often concerns itself with issues of efficiency,
optimization, integration and potential. The authors suggest that this pragmatism and instrumentality
of Open Science leaves it in line with prevailing political and institutional (i.e. neoliberal) aims.
Such a purely technocratic definition is no doubt at odds with the view of Open Science held by many
advocates. Yet the equivocal nature of Open Sciencemeans that such readings are at least plausible, and
hence should be taken as a call for the Open Science community to more fundamentally appraise the way
its priorities are presented, and the deeper ways in which societal voices can be engaged as equals in
setting research agendas. It further illuminates the ways in which the radicality of Open Science can
be questioned, and the extent to which Open Science merely enables the further neoliberalization and
commodification of research knowledge.
4. Discussion
This synthesis of evidence is intended to focus attention on the ways prevailing capacities, resources and
network centralitiescombined with structural inequalities and biasescan help shape Open Science
outcomes. Inequalities and dynamics of cumulative advantage pervade modern scholarship, and our
results show that despite its potential to improve equity in many areas, Open Science is not exempt.
Merton advises that cumulative advantage directs our attention to the ways in which initial
comparative advantages of trained capacity, structural location and available resources make for
successive increments of advantage such that the gaps between the haves and the have-nots in science
(as in other domains of social life) widen until dampened by countervailing processes[23]. From the
above synthesis we can observe that this mechanism is at work at various levels throughout Open
Science, potentially endangering equity. We have identified key areas for concern, summarized in table 2.
These issues can, in turn, be attributed in various ways to some fundamental concerns:
Ambiguity and politics: Open Science is an ambiguous and deeply political concept. We should not
expect that all of the diverse practices, much less their many possible routes to implementation,
that fall under this umbrella term should accord in every aspect. Equity is one aim among others
and may conflict with others like efficiency and transparency. The policy-driven focus on Open
Sciences potential to fuel economic growth, in particular, seems designed to maintain the
economic advantage and hence conflicts with wider aims of global equity. Moreover, narrow focus
on specific elements of Open Science, at the expense of a more holistic view of the
(dys)functioning of the scientific system as a whole, may exacerbate such factors.
Resource-intensity and network effects: Cumulative advantage relates to logics of accumulation and
preferential attachment based on network positionality and possession of resources. The resource-
intensive and networked nature of Open Science means it is also vulnerable to these logics.
Explicitly linking authorship channels to possession of resources potentially stratifies Open Access
publishing. The expensive infrastructures and training necessary to participate in engaging with
Open Data and methods means those privileged in these regards are primed to benefit most, at
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
13
Table 2. Summary of identied areas of concern for equity in Open Science.
aspect of Open Science area for concern group(s) most affected
general factors costs of participation: Open Science is resource-intensive in terms of infrastructure, support, training less well-resourced institutions and regions
political agendas: Open Science requires political will, but political agendas shape Open Science implementation. Especially
where economic growth is a stated ambition, this may be problematic
regions and institutions without such political backing, or where
political goals promote inequitable Open Science implementations
neoliberal logics: Open Science seen as potentially entrenching structures and ideologies of neoliberal commodication and
marketization of research knowledge as an economic resource to be exploited rather than as a common good for the
well-being of humanity
science per se, but especially those disciplines and researchers that
do not t this agenda
Open Access discriminatory business model: APC-based OA is exclusionary and risks stratifying authorship patterns less well-resourced researchers, institutions and regions. May also
impact specic demographics, including women
predatory publishing: limited issue which nonetheless primarily adversely affects non-dominant groups authors from developing nations and early career researchers
Open Data and FAIR
Data
situatedness of data practices: data practices are highly context-dependent, meaning one-size-ts-all policies risk privileging
some disciplines
qualitative researchers and disciplines
cumulative nature of data inequalities: creating and exploiting Open Data is strongly linked to access to infrastructure and
data literacy
less well-resourced researchers, institutions and regions
citation advantages of Open Data: Open Data seems linked to increased citations and hence early adopters benet (Matthew
effect)
less well-resourced researchers, institutions and regions
(Continued.)
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
14
Table 2. (Continued.)
aspect of Open Science area for concern group(s) most affected
Open Methods and Open
Infrastructure
transparency as a benchmark for quality: open methods require additional training, effort, infrastructure. Well-resourced and
high-status actors may potentially have an advantage
less well-resourced researchers, institutions and regions
reproducibility as a sine qua non for research: relatedly, meanings and limits of openness not uniform across disciplines.
Uncritically extending quantitative standards methodologies may obscure necessary interpretive work or further devalue
qualitative approaches
qualitative researchers and disciplines
platform-logic of Open Science: reliance on privately owned platforms may frustrate the aims of Open Science and increase
surveillance capitalism in academia
science as a whole
lack of reward structures for contributions to open infrastructure: Open Science seems at risk if it relies on closed and
proprietary systems; yet open infrastructures often rely on short-term project funding or volunteer labour which is not
properly rewarded within current incentive structures
early career researchers
Open Evaluation open identities peer review: peer review where reviewers are de-anonymized may either by discourage full and forthright
opinion or opening especially early career reviewers to potential future reprisals from aggrieved authors later on
erly career researchers, others from non-dominant groups
suitability of altmetrics as a tool for measuring impact: altmetrics criticized for: lack of robustness and susceptibility to
gaming; disparities of social media use between disciplines and geographical regions; reliance on commercial entities for
underlying data; indicating buzzrather than quality; underrepresentation of data from languages outside English;
exacerbating tyranny of metrics
all, especially non-English language research and areas where social
media use is less pronounced
Citizen Science logics of participation in Citizen Science: evidence of biased inclusion in populations invited to participate; potential for data
extraction absent anything else to echo colonial exploitation
the public, especially marginalized groups
interfaces with society,
industry, policy
resource-intensive nature of translational work: making outputs open is not enough to ensue uptake and societal impact.
The importance of (resource-intensive) translational work means richer institutions and regions may still dominate policy
conversations
less well-resourced researchers, institutions and regions
privileging of economic aims: the terms on which Open Science engages industry is asymmetrical, perhaps reecting the
importance of economic growth as a key aim. Industry is free to participate (or not) in open practices, as it suits them
science as a whole, but especially those domains not easily exploited
by commerce
exclusion of societal voices: Open Sciences terms of inclusion of publics is accused of instrumentalismand asymmetry
(experts/public)
the public
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
15
least initially. The importance of such underlying competences means that ensuring access is not
enough to ensure equity of opportunity in an Open Science world absent broader measures to
overcome the digital divide. In addition, the ways in which these underlying competences
interplay with actor attributes to shape logics of participation in networked communities means
that there are concerns for who is privileged by proposals to reform areas like peer review and
research evaluation, as well as who contributes to open source tools and services.
Narrow epistemologies: The term Open Science itself is often seen as exclusionary of the arts and
humanities in the anglophone community. More pernicious, though, is the potential devaluation
of epistemic diversity that attends Open Sciences focus on transparency and reproducibility.
Within the Open Science paradigm, the latter concepts are becoming almost synonyms for
research quality itself, rather than just important means of assuring quality in some domains.
Qualitative methodologies for which transparency is less possible and reproducibility less relevant
may be further marginalized if this trend continues. If so, Open Science will only add to the
further cumulative advantage of STEM subjects within the academy at the expense of social
sciences and humanities.
Neoliberal logics: In addition, certain central assumptions of Open Science seem to further promote the
commodification and marketization of research. Making science more responsive to the market risks
further intensifying competition at the expense of communalism. Further, platform-logic pervades
Open Sciences enabling infrastructures. Not only does this risk lock-in by commercial vendors,
but logics of data accumulation and tracking further enframe researchers as something to be
measured in a regime of surveillance capitalism. If so, Open Science may act to only further
advantage the neoliberalization of academia, which is often identified as a root cause of many of
the issues Open Science is claimed to fix.
5. Conclusion
The synthesis itself is subject to some key limitations, including some ironically linked to issues
discussed. The authors all work in the Global North at relatively well-resourced institutions and are
funded by an EC research grant whose conditions of application reflect a focus on the situation in
Europe. Article search was primarily via databases (Web of Science and Scopus) which employ strict
inclusionary criteria, and although this was combined with snow-balling and secondary literature
searching, it still might be the case that our review has not captured all available evidence. Further,
the language skills of the authors meant a pragmatic decision to only include articles in English. In
addition, reviews are necessarily retrospective. Although we have tried to be as balanced as possible,
these particularities of standpoint, resources and inclusion criteria no doubt influence our critique.
Our work here has been to scope the (English language) literature to date concerning threats to equity
within the transition to Open Science. This is of course preparatory work and by no means the end of the
story. Directions for future work may include first the extension of this study to cover literature in
languages beyond English. An additional study using the same methodology but involving a multi-
lingual team covering the major world languages could be envisioned, and the present authors would
be happy to collaborate in such an endeavour. Secondly, the issues raised in this work deserve much
more scrutiny and so future primary research work involving qualitative and quantitative approaches
on these issues is desired. Finally, this work has aimed primarily to scope the issues involved and is
not strongly normative in the sense of producing specific recommendations on what policy actions
may be suggested to correct potential negative effects on equity in the transition to Open Science.
Such recommendations are no doubt required.
We hope that the wider Open Science community will take these criticisms in the constructive spirit
in which they are meant, as a springboard to help recognize and further address such issues. As stated
earlier, none of this is meant to diminish the aims of Open Science per se, or negate the good that Open
Science brings and has the potential to bring. Rather, it is to align ourselves with Fernández Pinto [15],
who questions the particular way the ideal has been conceived and implemented by the Open Science
movement, as well as the way it has been brought about through Open Science policies. In this sense, the
faulty logic of open science that I aim to highlight refers precisely to the inconsistency between the
ideal and its current implementation. Given its commonly held aim of increasing equity, any
potential for Open Science to actually drive inequalities must be taken seriously by the scientific
community in order to realize the aim of making science truly open, collaborative and meritocratic.
Data accessibility. Protocol: https://osf.io/t6uy9/. Data: https://doi.org/10.5281/zenodo.4936203.
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
16
The data are provided in the electronic supplementary material [264].
Authorscontributions. Conceptualization: T.R.-H., S.R.; data curation: T.R.-H., S.R.; funding acquisition: T.R.-H.;
investigation: T.R.-H., S.R., N.L.C., A.F., T.K., N.P.; methodology: T.R.-H., S.R.; project administration: T.R.-H., S.R.;
resources: T.R.-H.; supervision: T.R.-H.; validation: T.R.-H.; writingoriginal draft: T.R.-H., S.R., N.L.C., A.F., T.K.,
N.P.; writingreview and editing: T.R.-H., S.R., N.L.C., A.F., T.K., N.P.
All authors gave final approval for publication and agreed to be held accountable for the work performed therein.
Competing interests. We declare we have no competing interests.
Funding. This work was supported by the project ON-MERRIT, funded by the European Commission under the Horizon
2020 programme (grant no. 824612). The Know-Center is funded within COMETCompetence Centers for Excellent
Technologiesunder the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the
Austrian Federal Ministry of Economy, Family and Youth and by the State of Styria. COMET is managed by the
Austrian Research Promotion Agency FFG.
Acknowledgements. The authors gratefully thank the following for valuable comments on earlier drafts of this article: three
reviewers for Royal Society Open Science, Bernhard Wieser (via email), Benedikt Fecher (via email), Daniel Lakens (via
Hypothesis), Egon Willighagen (via Hypothesis) and Cameron Neylon (via Twitter). Any errors remain entirely the
authorsown.
References
1. Cash-Gibson L, Rojas-Gualdrón DF, Pericàs JM,
Benach J. 2018 Inequalities in global health
inequalities research: a 50-year bibliometric
analysis (19662015). PLoS ONE 13, e0191901.
(doi:10.1371/journal.pone.0191901)
2. Monroe-White T, Woodson TS. 2016 Inequalities
in scholarly knowledge: public value failures
and their impact on global science. Afr. J. Sci.
Technol. Innov. Dev. 8, 178186. (doi:10.1080/
20421338.2016.1147204)
3. Moore S, Neylon C, Paul Eve M, Paul ODonnell
D, Pattinson D. 2017 Excellence R Us: university
research and the fetishisation of excellence.
Palgrave Commun. 3,113. (doi:10.1057/
s41599-017-0001-8)
4. Noble P, Eyck PT, Roskoski Jr R, Jackson JB. 2020
NIH funding trends to US medical schools from
2009 to 2018. PLoS ONE 15, e0233367. (doi:10.
1371/journal.pone.0233367)
5. Bol T, Vaan Md, Rijt Avd. 2018 The Matthew
effect in science funding. Proc. Natl Acad. Sci.
USA 115, 48874890. (doi:10.1073/pnas.
1719557115)
6. Brown JVE, Crampton PES, Finn GM, Morgan JE.
2020 From the sticky floor to the glass ceiling
and everything in between: protocol for a
systematic review of barriers and facilitators to
clinical academic careers and interventions to
address these, with a focus on gender
inequality. Syst. Rev. 9,17. (doi:10.1186/
s13643-019-1259-2)
7. Poczatková B, Křibíko P. 2017 Gender
inequality in the field of science and research.
J. Int. Stud. 10, 267276. (doi:10.14254/2071-
8330.2017/10-1/19)
8. Cech EA, Blair-Loy M. 2010 Perceiving glass
ceilings? Meritocratic versus structural
explanations of gender inequality among
women in science and technology. Soc.
Probl. 57, 371397. (doi:10.1525/sp.2010.
57.3.371)
9. Penner AM. 2015 Gender inequality in science.
Science 347, 234235. (doi:10.1126/science.aaa3781)
10. Gesiarz F, de Neve J-E, Sharot T. 2020 The
motivational cost of inequality: opportunity
gaps reduce the willingness to work. PLoS ONE
15, e0237914. (doi:10.1371/journal.pone.
0237914)
11. Zuckerman H. 1988 The sociology of science.
In Handbook of sociology (ed. NJ Smelser),
pp. 511574, Thousand Oaks, CA: Sage
Publications, Inc.
12. Bourdieu P. 1975 The specificity of the scientific
field and the social conditions of the progress of
reason. Soc. Sci. Inf. 14,1947. (doi:10.1177/
053901847501400602)
13. Vicente-Saez R, Martinez-Fuentes C. 2018
Open Science now: a systematic literature review for
an integrated definition. J. Bus. Res. 88,
428436. (doi:10.1016/j.jbusres.2017.12.043)
14. Pontika N, Knoth P, Cancellieri M, Pearce S. 2015
Fostering open scienceto researchusing a taxonomy
and an eLearning portal.InProc. 15th Int. Conf. on
Knowledge Technologies and Data-driven Business,
pp. 11:111:8. New York, NY: ACM.
15. Fernández Pinto M. 2020. Open Science for
private Interests? How the logic of open science
contributes to the commercialization of
research. Front. Res. Metr. Anal. 5, 588331.
(doi:10.3389/frma.2020.588331)
16. Fecher B, Friesike S. 2014 Open science: one
term, five schools of thought. In Opening science
(eds S Bartling, S Friesike), pp. 1747. Cham,
Switzerland: Springer International Publishing.
17. Chan L et al. 2002 Budapest Open Access
Initiative. See https://web.archive.org/web/
20171213093708/http://www.
budapestopenaccessinitiative.org/read.
18. Nielsen M. 2013 Reinventing discovery: the new
era of networked science. Princeton, NJ:
Princeton University Press.
19. Ali-Khan SE, Jean A, MacDonald E, Gold ER.
2018 Defining success in open science. MNI
Open Res. 2, 2. (doi:10.12688/mniopenres.
12780.1)
20. Grahe JE, Cuccolo K, Leighton DC, Alvarez LDC.
2020 Open science promotes diverse, just, and
sustainable research and educational outcomes.
Psychol. Learn. Teach. -Plat. 19,520. (doi:10.
1177/1475725719869164)
21. Merton RK. 1968 The Matthew effect in science:
the reward and communication systems of
science are considered. Science 159,5663.
(doi:10.1126/science.159.3810.56)
22. Merton RK. 1973 The normative structure of
science. In The sociology of science, pp. 267280.
Chicago, IL: The University of Chicago Press.
23. Merton RK. 1988 The Matthew effect in science,
II. Cumulative advantage and the symbolism of
intellectual property. Isis 79, 606623. (doi:10.
1086/354848)
24. Wang J. 2014 Unpacking the Matthew effect in
citations. J. Informetr. 8, 329339. (doi:10.
1016/j.joi.2014.01.006)
25. Larivière V, Gingras Y. 2010 The impact factors
Matthew effect: a natural experiment in
bibliometrics. J. Am. Soc. Inf. Sci. Technol. 61,
424427. (doi:10.1002/asi.21226)
26. Langfeldt L, Benner M, Sivertsen G, Kristiansen
EH, Aksnes DW, Borlaug SB, Hansen HF, Kallerud
E, Pelkonen A. 2015 Excellence and growth
dynamics: a comparative study of the Matthew
effect. Sci. Public Policy 42, 661675. (doi:10.
1093/scipol/scu083)
27. Weakliem DL, Gauchat G, Wright BRE. 2012
Sociological stratification: change and continuity
in the distribution of departmental prestige,
19652007. Am. Sociol. 43, 310327. (doi:10.
1007/s12108-011-9133-2)
28. Bonitz M, Bruckner E, Scharnhorst A. 1999 The
Matthew indexconcentration patterns and
Matthew core journals. Scientometrics 44, 361.
(doi:10.1007/BF02458485)
29. Hofmänner A. 2011 The African Eve effect in
science. Archaeologies 7, 251289. (doi:10.
1007/s11759-011-9160-1)
30. Rossiter MW. 1993 The Matthew Matilda effect
in science. Soc. Stud. Sci. 23, 325341. (doi:10.
1177/030631293023002004)
31. Squazzoni F, Gandelli C. 2012 Saint Matthew
strikes again: an agent-based model of peer
review and the scientific community structure.
J. Informetr. 6, 265275. (doi:10.1016/j.joi.
2011.12.005)
32. Woods J. 2015 The Op-ed sociologists: the
Matthew effect in Op-ed publication patterns.
Am. Sociol. 46, 356372. (doi:10.1007/s12108-
015-9269-6)
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
17
33. Zhi Q, Meng T. 2016 Funding allocation,
inequality, and scientific research output: an
empirical study based on the life science sector
of Natural Science Foundation of China.
Scientometrics 106, 603628. (doi:10.1007/
s11192-015-1773-5)
34. Berbeglia F, Hentenryck PV. 2017 Taming the
Matthew effect in online markets with social
influence.InProc. 31st AAAI Conf. on Artificial
Intelligence, pp. 1016. San Francisco, CA: AAAI
Press.
35. Stanovich KE. 2009 Matthew effects in reading:
some consequences of individual differences in
the acquisition of literacy. J. Educ. 189,2355.
(doi:10.1177/0022057409189001-204)
36. Blasio BF de, Svensson Å, Liljeros F. 2007
Preferential attachment in sexual networks.
Proc. Natl Acad. Sci. USA 104, 10 76210 767.
(doi:10.1073/pnas.0611337104)
37. Lindenfors P, Wilson M, Lindberg SI. 2020 The
Matthew effect in political science: head start
and key reforms important for democratization.
Humanit. Soc. Sci. Commun. 7,14. (doi:10.
1057/s41599-020-00596-7)
38. DiPrete TA, Eirich GM. 2006 Cumulative
advantage as a mechanism for inequality: a
review of theoretical and empirical
developments. Annu. Rev. Sociol. 32, 271297.
(doi:10.1146/annurev.soc.32.061604.123127)
39. Barabási A-L, Albert R. 1999 Emergence of
scaling in random networks. Science 286,
509512. (doi:10.1126/science.286.5439.509)
40. Albornoz D, Huang M, Martin IM, Mateus M,
Touré AY, Chan L. 2020 Framing power: tracing
key discourses in open science policies.In22nd
Int. Conf. on Electronic Publishing. Connecting
the Knowledge Commons: From Projects to
Sustainable Infrastructure, ELPUB 2018, Toronto,
Canada, June 2018. (doi:10.4000/proceedings.
elpub.2018.23)
41. Mirowski P. 2018 The future(s) of open science.
Soc. Stud. Sci. 48, 171203. (doi:10.1177/
0306312718772086)
42. Page MJ et al. 2021 The PRISMA 2020
statement: an updated guideline for reporting
systematic reviews. Br. Med. J. 372, n71.
(doi:10.1136/bmj.n71)
43. McKiernan EC et al. 2016 How open science
helps researchers succeed. eLife 5, e16800.
(doi:10.7554/eLife.16800)
44. Munafò MR et al. 2017 A manifesto for
reproducible science. Nat. Hum. Behav. 1, 0021.
(doi:10.1038/s41562-016-0021)
45. Fell MJ. 2019 The economic impacts of Open
Science: a rapid evidence assessment.
Publications 7, 46. (doi:10.3390/
publications7030046)
46. Tennant JP, Waldner F, Jacques DC, Masuzzo P,
Collister LB, Hartgerink CH. 2016 The academic,
economic and societal impacts of Open Access:
an evidence-based review. F1000Research 5,
632. (doi:10.12688/f1000research.8460.3)
47. Suber P. 2012 Open access. Cambridge, MA: MIT
Press.
48. Willinsky J. 2009 The access principle: the case
for open access to research and scholarship.
Cambridge, MA: MIT Press.
49. Wilkinson MD et al. 2016 The FAIR Guiding
Principles for scientific data management and
stewardship. Sci. Data. 3, 160018. (doi:10.1038/
sdata.2016.18)
50. Wilsdon J et al. 2015 The metric tide. Report of
the Independent Review of the Role of Metrics
in Research Assessment and Management. See
http://rgdoi.net/10.13140/RG.2.1.4929.1363.
51. Ross-Hellauer T. 2017 What is open peer
review? A systematic review. F1000Research 6,
588. (doi:10.12688/f1000research.11369.1)
52. Chin JM, Ribeiro G, Rairden A. 2019 Open
forensic science. J. Law Biosci. 6, 255288.
(doi:10.1093/jlb/lsz009)
53. Tenopir C et al. 2017 Research data services in
European academic research libraries. LIBER Q.
27,2344. (doi:10.18352/lq.10180)
54. MoRRI consortium. 2018 Final report:
summarising insights from the MoRRI project.
See http://morri-project.eu/reports/2018-05-24-
final-report-summarising-insights-from-the-
morri-project.
55. Tenopir C, Sandusky RJ, Allard S, Birch B. 2014
Research data management services in
academic research libraries and perceptions of
librarians. Libr. Inf. Sci. Res. 36,8490. (doi:10.
1016/j.lisr.2013.11.003)
56. Siriwardhana C. 2015 Promotion and reporting
of research from resource-limited settings.
Infect. Dis. 8,2529.
57. Steinhardt I. 2020 Learning Open Science by
doing Open Science. A reflection of a qualitative
research project-based seminar. Educ. Inf. 36,
263279.
58. Maiti D, Castellacci F, Melchior A. 2019
Digitalisation and development: issues for India
and beyond. See https://www.scopus.com/
inward/record.uri?eid=2-s2.0-
85088466409&doi=10.1007%2f978-981-13-
9996-1&partnerID=40&md5=
ecc493ceb8a76e4c121d1b304ced9b35.
59. Prainsack B, Leonelli S. 2018 Responsibility.
In Science and the politics of openness
(eds B Nerlich, S Hartley, S Raman, A Smith),
pp. 97107. Manchester, UK: Manchester
University Press.
60. Sveinsdottir T, Proudman V, Davidson J. 2020 An
analysis of open science policies in Europe, v6.
See https://zenodo.org/record/4005612#.
YGg9YugzY2w.
61. Burgelman J-C. 2021 Politics and Open Science:
how the European open science cloud became
reality (the untold story). Data Intell. 3,519.
(doi:10.1162/dint_a_00069)
62. Directorate-General for Research and Innovation.
2016 Open innovation, open science, open to the
world: a vision for Europe. Luxembourg:
Publications Office of the European Union.
63. Slaughter S, Rhoades G. 2000 The neo-liberal
university. New Labor Forum 6,7379. (http://
www.jstor.org/stable/40342886)
64. Maisuria A, Cole M. 2017 The neoliberalization
of higher education in England: an alternative is
possible. Policy Futur. Educ. 15, 602619.
(doi:10.1177/1478210317719792)
65. Canaan JE, Shumar W. 2008 Structure and agency
in the neoliberal university. Oxford, UK:
Routledge.
66. Mirowski P, Sent E-M. 2002 Science bought and
sold: essays in the economics of science. Chicago,
IL: University of Chicago Press.
67. Tyfield D. 2013 Transition to Science 2.0:
Remoralizingthe economy of science.
Spontaneous Gener. J. Hist. Philos. Sci. 7,2948.
68. Kansa EC. 2014 The need to humanize open
science. In Issues in open research data (ed. S
Moore), pp. 3158. London, UK: Ubiquity Press.
69. Fitzpatrick K. 2011 Planned obsolescence:
publishing, technology, and the future of the
academy. New York, NY: NYU Press.
70. Mayer KU. 2013 Open Access improves returns
to public research funding: a perspective from
Germany. Inf. Serv. Use 33,310.
71. Nwagwu WE, Ahmed A. 2009 Building open
access in Africa. Int. J. Technol. Manag. 45,
82101. (doi:10.1504/IJTM.2009.021521)
72. Bawa AC. 2020 South Africas journey towards
open access publishing. Biochemist 42,3033.
(doi:10.1042/BIO20200029)
73. Koutras N. 2020 The public policy basis for open
accesspublishing:a scientific approach. Publ. Res.Q.
36,538552. (doi:10.1007/s12109-020-09772-8)
74. Arunachalam S. 2017 Social justice in scholarly
publishing: open access is the only way.
Am. J. Bioeth. 17,1517. (doi:10.1080/
15265161.2017.1366194)
75. Raju R, Claassen J, Moll E. 2016 Researchers
adapting to open access journal publishing: the
case of the University of Cape Town. South
Afr. J. Libr. Inf. Sci. 82,3445.
76. Koutras N. 2015 The open access in the context
of the globalizing world. Publ. Res. Q. 31,
132141. (doi:10.1007/s12109-015-9403-x)
77. Chan L, Arunachalam S, Kirsop B. 2009
Open access: a giant leap towards bridging
health inequities. Bull. World Health Organ.
87, 631635. (doi:10.2471/BLT.09.064659)
78. Terry R. 2009 Building a bridge for research.
Bull. World Health Organ. 87, 636. (doi:10.2471/
BLT.09.069286)
79. Robinson M, Scherlen A. 2009 Publishing in
criminology and criminal justice:
assessing journal editorsawareness and
acceptance of open access. Int. J. Crim. Justice
Sci. 4,98117.
80. Scherlen A, Robinson M. 2008 Open access to
criminal justice scholarship: a matter of social
justice. J. Crim. Justice Educ. 19,5474. (doi:10.
1080/10511250801892961)
81. Adelle C. 2019 The role of knowledge in food
democracy. Polit. Gov. 7, 214223.
82. Roehrig AD, Soper D, Cox BE, Colvin GP. 2018
Changing the default to support open access to
education research. Educ. Res. 47, 465473.
(doi:10.3102/0013189X18782974)
83. Moore S. 2019 Common struggles: policy-based
vs. scholar-led approaches to open access in the
humanities. See https://hcommons.org/deposits/
item/hc:24135/.
84. Fuchs C, Sandoval M. 2013 The diamond model
of open access publishing: why policy makers,
scholars, universities, libraries, labour unions
and the publishing world need to take non-
commercial, non-profit open access serious.
TripleC 11, 428443. (doi:10.31269/triplec.
v11i2.502)
85. Raju R, Claassen J, Pietersen J, Abrahamse D.
2020 An authentic flip subscription model for
Africa: library as publisher service. Libr. Manag.
41, 369381. (doi:10.1108/LM-03-2020-0054)
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
18
86. Willetts D. 2012 Accessibility, sustainability,
excellence: how to expand access to research
publications. Finch Group report: government
response on open access. See https://www.gov.
uk/government/publications/letter-to-dame-
janet-finch-on-the-government-response-to-
the-finch-group-report-accessibility-
sustainability-excellence-how-to-expand-access-
to-research-publications.
87. Debat H, Babini D. 2019 Plan S: take Latin
Americas long experience on board. Nature
573, 495. (doi:10.1038/d41586-019-02857-1)
88. Bosman J, Frantsvåg JE, Kramer B, Langlais P-C,
Proudman V. 2021 OA Diamond Journals Study.
Part 1: findings. Zenodo. See https://zenodo.
org/record/4558704#.YHwQL-gzY2w.
89. Becerril A, Bosman J, Bjørnshauge L, Frantsvåg
JE, Kramer B, Langlais P-C, Torny D. 2021 OA
Diamond Journals Study. Part 2:
recommendations. Zenodo. See https://zenodo.
org/record/4562790#.YHwQL-gzY2w.
90. Pourret O, Hursthouse A, Irawan DE,
Johannesson K, Liu H, Poujol M, Tartese R, van
Hullebusch ED, Wiche O. 2020 Open Access
publishing practice in geochemistry: overview of
current state and look to the future. Heliyon 6,
e03551. (doi:10.1016/j.heliyon.2020.e03551)
91. Boudry C et al. 2019 Worldwide inequality in
access to full text scientific articles: the example
of ophthalmology. PeerJ 7, e7850. (doi:10.7717/
peerj.7850)
92. Siler K, Haustein S, Smith E, Larivière V, Alperin
JP. 2018 Authorial and institutional stratification
in open access publishing: the case of global
health research. PeerJ 6, e4269. (doi:10.7717/
peerj.4269)
93. Batterbury S. 2017 Socially just publishing:
implications for geographers and their journals.
Fenn.-Int. J. Geogr. 195, 175181. (doi:10.
11143/fennia.66910)
94. Sotudeh H, Horri A. 2008 Great expectations:
the role of Open Access in improving countries
recognition. Scientometrics 76,6993. (doi:10.
1007/s11192-007-1890-x)
95. Gray RJ. 2020 Sorry, were open: golden open-
access and inequality in non-human biological
sciences. Scientometrics 124, 16631675.
(doi:10.1007/s11192-020-03540-3)
96. Christian GE. 2008 Open access initiative and the
developing world. Afr. J. Libr. Arch. Inf. Sci. 18,
111.
97. Davison RM, Harris RW, Licker PL, Shoib G. 2005
Open access e-journals: creating sustainable
value in developing countries. In 9th Pacific Asia
Conf. on Information Systems: IT and Value
Creation, PACIS 2005, pp. 248256. See https://
www.scopus.com/inward/record.uri?eid=2-s2.0-
67650063020&partnerID=40&md5=
dce8fb0f75934413518c120b57b7f7b9.
98. Ellers J, Crowther TW, Harvey JA. 2017 Gold
open access publishing in mega-journals:
developing countries pay the price of western
premium academic output. J. Sch. Publ. 49,
89102. (doi:10.3138/jsp.49.1.89)
99. Tennant JP, Lomax DR. 2019 An overview of open
access publishing in palaeontology. Palaeontol.
Electron. 22,110. (doi:10.26879/968)
100. Monge-Nájera J, Monge-Nájera J. 2018 Please
pay $800 to read this article: an open letter to
paywall companies. Rev. Biol. Trop. 66,15.
(doi:10.15517/rbt.v66i1.32238)
101. Copiello S. 2020 Business as usual with article
processing carges in the transition towards OA
publishing: a case study based on Elsevier.
Publications 8, 3. (doi:10.3390/publications8010003)
102. Jubb M, Plume A, Oeben S, Brammer L,
Johnson R, Bütün C, Pinfield S.
2017. Monitoring the transition to open access.
London, UK: Universities UK. See https://eprints.
whiterose.ac.uk/125509/.
103. Lawson S. 2015 Fee waivers for open access
journals. Publications 3, 155167. (doi:10.3390/
publications3030155)
104. Burchardt J. 2014 Researchers outside APC-
financed open access: implications for scholars
without a paying institution. SAGE Open 4,
2158244014551714. (doi:10.1177/
2158244014551714)
105. Niles MT, Schimanski LA, McKiernan EC, Alperin
JP. 2020 Why we publish where we do: faculty
publishing values and their relationship to
review, promotion and tenure expectations.
PLoS ONE 15, e0228914. (doi:10.1371/journal.
pone.0228914)
106. Ottaviani J. 2016 The post-embargo open access
citation advantage: it exists (probably), its
modest (usually), and the rich get richer (of
course). PLoS ONE 11, e0159614. (doi:10.1371/
journal.pone.0159614)
107. Ferrer-Sapena A, Sánchez-Pérez EA, Peset F,
González L-M, Aleixandre-Benavent R. 2016 The
impact factor as a measuring tool of the
prestige of the journals in research assessment
in mathematics. Res. Eval. 25, 306314.
(doi:10.1093/reseval/rvv041)
108. Else H. 2020 Nature journals reveal terms of
landmark open-access option. Nature 588,
1920. (doi:10.1038/d41586-020-03324-y)
109. Demeter M, Istratii R. 2020 Scrutinising what
open access journals mean for global
inequalities. See https://www.scopus.com/
inward/record.uri?eid=2-s2.0-
85095569661&doi=10.1007%2fs12109-020-
09771-9&partnerID=40&md5=
b86a8ec65f5e459e1b2bc5c1f6e56003.
110. Eve MP. 2014 Open access and the humanities.
Cambridge, UK: Cambridge University Press.
111. Génova G, de la Vara JL. 2019 The problem is
not professional publishing, but the publish-or-
perish culture. Sci. Eng. Ethics 25, 617619.
(doi:10.1007/s11948-017-0015-z)
112. Grudniewicz A et al. 2019 Predatory journals: no
definition, no defence. Nature 576, 210212.
(doi:10.1038/d41586-019-03759-y)
113. Eve MP, Priego E. 2017 Who is actually harmed
by predatory publishers? TripleC Commun.
Capital. Crit. Open Access J. Glob. Sustain. Inf.
Soc. 15, 755770.
114. Shen C, Björk B-C. 2015 Predatoryopen access:
a longitudinal study of article volumes and
market characteristics. BMC Med. 13, 230.
(doi:10.1186/s12916-015-0469-2)
115. MoChridhe R. 2019 Linguistic equity as open
access: internationalizing the language of
scholarly communication. J. Acad. Librariansh.
45, 423427. (doi:10.1016/j.acalib.2019.02.006)
116. Ramírez-Castañeda V. 2020 Disadvantages in
preparing and publishing scientific papers
caused by the dominance of the English
language in science: the case of Colombian
researchers in biological sciences. PLoS ONE 15,
e0238372. (doi:10.1371/journal.pone.0238372)
117. Williams-Jones B, Couture V, Boulanger R, Dupras
C. 2017 Imaginingtruly open accessbioethics: from
dreams to reality. Am. J. Bioeth. 17,1920.
(doi:10.1080/15265161.2017.1365193)
118. Foxall K. 2019 The current state of African
oncology research publication: how to increase
Africas research impact. Ecancermedicalscience
13, ed93. (doi:10.3332/ecancer.2019.ed 93)
119. James JE. 2017 Free-to-publish, free-to-read, or
both? Cost, equality of access, and integrity in
science publishing. J. Assoc. Inf. Sci. Technol. 68,
15841589. (doi:10.1002/asi.23757)
120. Nnaji JC. 2018 Illegitimate academic publishing: a
need for sustainable global action. Publ. Res. Q.
34,515528. (doi:10.1007/s12109-018-9614-z)
121. Soler J, Cooper A. 2019 Unexpected emails to
submit your work: spam or legitimate offers?
The implications for novice English L2 writers.
Publications 7, 7. (doi:10.3390/
publications7010007)
122. Allman D. 2019 Pseudo or perish:
problematizing the predatoryin global health
publishing. Crit. Public Health 29, 413423.
(doi:10.1080/09581596.2019.1606417)
123. Noga-Styron KE, Olivero JM, Britto S. 2017
Predatory journals in the criminal justices
sciences: getting our cite on the target. J. Crim.
Justice Educ. 28, 174191. (doi:10.1080/
10511253.2016.1195421)
124. Kurt S. 2018 Why do authors publish in
predatory journals? Learn. Publ. 31, 141147.
(doi:10.1002/leap.1150)
125. Collyer FM. 2018 Global patterns in the
publishing of academic knowledge: global
North, global South. Curr. Sociol. 66,5673.
(doi:10.1177/0011392116680020)
126. Garuba AR. 2013 The prospects of bridging the
digital divide in Africa. Libr. Philos. Pract. 2013,
882.
127. Nyamnjoh F. 2010 Institutional review: open
access and open knowledge production
processes: lessons from CODESRIA. Institutional
Rev. 10,6772. (doi:10.23962/10539/19772)
128. Czerniewicz L. 2015 Opinion: confronting
inequitable power dynamics of global
knowledge production and exchange. Water
Wheel 14,2629.
129. Piwowar HA, Day RS, Fridsma DB. 2007 Sharing
detailed research data is associated with
increased citation rate. PLoS ONE 2, e308.
(doi:10.1371/journal.pone.0000308)
130. Gilmore RO, Diaz MT, Wyble BA, Yarkoni T. 2017
Progress toward openness, transparency, and
reproducibility in cognitive neuroscience.
Ann. N. Y. Acad. Sci. 1396,518. (doi:10.1111/
nyas.13325)
131. Toelch U, Ostwald D. 2018 Digital open
scienceteaching digital tools for reproducible
and transparent research. PLoS Biol. 16,
e2006022. (doi:10.1371/journal.pbio.2006022)
132. Fecher B, Friesike S, Hebing M. 2015 What
drives academic data sharing? PLoS ONE 10,
e0118053. (doi:10.1371/journal.pone.0118053)
133. Linek SB, Fecher B, Friesike S, Hebing M. 2017
Data sharing as social dilemma: influence of the
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
19
researchers personality. PLoS ONE 12,
e0183216. (doi:10.1371/journal.pone.0183216)
134. Leonelli S, Spichtinger D, Prainsack B. 2015
Sticks and carrots: encouraging open science at
its source. Geo 2,1216.
135. Olmos-Peñuela J, Benneworth P, Castro-
Martínez E. 2015 What stimulates researchers to
make their research usable? Towards an
Opennessapproach. Minerva 53, 381410.
(doi:10.1007/s11024-015-9283-4)
136. Rappert B, Bezuidenhout L. 2016 Data sharing
in low-resourced research environments.
Prometheus 34, 207224. (doi:10.1080/
08109028.2017.1325142)
137. Borgman CL. 2015 Big data, little data, no data:
scholarship in the networked world. Cambridge,
MA: The MIT Press.
138. Leonelli S. 2016 Data-centric biology. A
philosophical study. Chicago, IL: The University
of Chicago Press.
139. Borgman CL. 2012 The conundrum of sharing
research data. J. Am. Soc. Inf. Sci. Technol. 63,
10591078. (doi:10.1002/asi.22634)
140. Hillyer R, Posada A, Albornoz D, Chan L, Okune
A. 2017 Framing a situated and inclusive open
science: emerging lessons from the open and
collaborative science in development network.
In Expanding perspectives on open science:
communities, cultures and diversity in concepts
and practices, pp. 1833. Clifton, VA: IOS Press.
141. Perrier L, Blondal E, Ayala AP, Dearborn D. 2017
Research data management in academic
institutions: a scoping review. PLoS ONE 12,
e0178261. (doi:10.1371/journal.pone.0178261)
142. Bezuidenhout LM, Leonelli S, Kelly AH, Rappert
B. 2017 Beyond the digital divide: towards a
situated approach to open data. Sci. Public
Policy 44, 464475. (doi:10.1093/scipol/
scw036)
143. Weinshall K, Epstein L. 2020 Developing high-
quality data infrastructure for legal analytics:
introducing the Israeli Supreme Court Database.
J. Empir. Leg. Stud. 17, 416434. (doi:10.1111/
jels.12250)
144. Cychosz M et al. 2020 Longform recordings of
everyday life: ethics for best practices. Behav.
Res. Methods 52, 19511969. (doi:10.3758/
s13428-020-01365-9)
145. Bargh MS, Meijer R, Vink M, van den Braak S,
Choenni S. 2019 On opening sensitive data sets
in light of GDPR.InProc. 12th Int. Conf. on
Theory and Practice of Electronic Governance
(icegov2019),Melbourne, Australia, April 2019,
pp. 510512. New York, NY: ACM. (doi:10.1145/
3326365.3326444).
146. Ross MW, Iguchi MY, Panicker S. 2018 Ethical
aspects of data sharing and research participant
protections. Am. Psychol. 73, 138145. (doi:10.
1037/amp0000240)
147. Johnson JA. 2014 From open data to
information justice. Ethics Inf. Technol. 16,
263274. (doi:10.1007/s10676-014-9351-8)
148. Johnson JA. 2018 Open data, big data, and just
data. Public Adm. Inf. Technol. 33,2349.
(doi:10.2307/974782)
149. Cinnamon J. 2020 Data inequalities and why
they matter for development. Inf. Technol. Dev.
26, 214233. (doi:10.1080/02681102.2019.
1650244)
150. Klump J. 2017 Data as social capital and the gift
culture in research. Data Sci. J. 16, 14. (doi:10.
5334/dsj-2017-014)
151. Bezuidenhout L, Kelly AH, Leonelli S, Rappert B.
2017 $100 is not much to you: open science
and neglected accessibilities for scientific
research in Africa. Crit. Public Health 27,3949.
(doi:10.1080/09581596.2016.1252832)
152. Atenas J, Havemann L, Timmermann C. 2020
Critical literacies for a datafied society: academic
development and curriculum design in higher
education. Res. Learn. Technol. 28, 2468.
(doi:10.25304/rlt.v28.2468)
153. Yoon A, Copeland A. 2019 Understanding social
impact of data on local communities.
Aslib. J. Inf. Manag. 71, 558567. (doi:10.1108/
AJIM-12-2018-0310)
154. DIgnazio C, Bhargava R. 2018 Creative data
literacy: a constructionist approach to teaching
information visualization. Digit. Humanit. Q.
12, 03.
155. Edelenbos J, Hirzalla F, van Zoonen L, van Dalen
J, Bouma G, Slob A, Woestenburg A. 2018
Governing the complexity of smart data cities:
setting a research agenda. Public Adm. Inf.
Technol. 24,3554.
156. Carroll SR, Rodriguez-Lonebear D, Martinez A.
2019 Indigenous data governance: strategies
from United States native nations. Data Sci. J.
18, 31. (doi:10.5334/dsj-2019-031)
157. Kitchin R. 2013 Four critiques of open data
initiatives. Impact of Social Sciences. See https://
blogs.lse.ac.uk/impactofsocialsciences/2013/11/
27/four-critiques-of-open-data-initiatives/.
158. Larivière V, Pontille D, Sugimoto CR. 2020
Investigating the division of scientific labor
using the Contributor Roles Taxonomy (CRediT).
Quant. Sci. Stud. 2,1128. (doi:10.1162/qss_a_
00097)
159. Chambers CD. 2013 Registered reports: a new
publishing initiative at cortex. Cortex J. Devoted
Study Nerv. Syst. Behav. 49, 609610. (doi:10.
1016/j.cortex.2012.12.016)
160. Nosek BA et al. 2015 Promoting an open
research culture. Science 348, 14221425.
(doi:10.1126/science.aab2374)
161. Rogers EM. 2003 Diffusion of innovations, 5th
edn. New York, NY: Free Press.
162. Leonelli S. 2018 Rethinking reproducibility as a
criterion for research quality. In Research in the
history of economic thought and methodology
(eds L Fiorito, S Scheall, CE Suprinyak), pp.
129146. Bingley, UK: Emerald Publishing
Limited.
163. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA,
Flint J, Robinson ESJ, Munafò MR. 2013 Power
failure: why small sample size undermines the
reliability of neuroscience. Nat. Rev. Neurosci.
14, 365376. (doi:10.1038/nrn3475)
164. John LK, Loewenstein G, Prelec D. 2012
Measuring the prevalence of questionable
research practices with incentives for truth
telling. Psychol. Sci. 23, 524532. (doi:10.1177/
0956797611430953)
165. Penders B, Holbrook JB, De Rijcke S. 2019 Rinse
and repeat: understanding the value of
replication across different ways of knowing.
Publications 7, 52. (doi:10.3390/
publications7030052)
166. Freese J, Peterson D. 2017 Replication in social
science. Annu. Rev. Sociol. 43, 147165. (doi:10.
1146/annurev-soc-060116-053450)
167. Li D, Koedel C. 2017 Representation and salary
gaps by race-ethnicity and gender at selective
public universities. Educ. Res. 46, 343354.
(doi:10.3102/0013189X17726535)
168. Andrews P. 2020 The platformization of open. In
Reassembling scholarly communications:
histories, infrastructures, and global politics of
open access (eds MP Eve, J Gray), pp. 265276.
Cambridge, MA: The MIT Press.
169. Plantin J-C, Lagoze C, Edwards PN. 2018 Re-
integrating scholarly infrastructure: the
ambiguous role of data sharing platforms. Big
Data Soc. 5, 2053951718756683.
170. Posada A, Chen G. 2018 Inequality in
knowledge production: the integration of
academic infrastructure by big publishers.
ELPUB2018,Toronto, Canada,June 2018. CCSD,
France: Episciences. (doi:10.4000/proceedings.
elpub.2018.30)
171. Ross-Hellauer T, Schmidt B, Kramer B. 2018 Are
funder open access platforms a good idea? SAGE
Open 8, 2158244018816717. (doi:10.1177/
2158244018816717)
172. Okune A, Hillyer R, Albornoz D, Posada A, Chan
L. 2018 Whose infrastructure? Towards inclusive
and collaborative knowledge infrastructures in
open science. ELPUB2018,Toronto, Canada,June
2018. CCSD, France: Episciences. (doi:10.4000/
proceedings.elpub.2018.31)
173. Thanos C. 2016 A vision for open cyber-scholarly
infrastructures. Publications 4, 13. (doi:10.3390/
publications4020013)
174. Bilder G, Lin J, Neylon C. 2015 Principles for
open scholarly infrastructures-v1. See http:///
articles/journal_contribution/Principles_for_
Open_Scholarly_Infrastructures_v1/1314859/1.
175. Hall G. 2015 What does Academia_edus
success mean for Open Access? The data-driven
world of search engines and social networking.
Impact of Social Sciences. See https://blogs.lse.
ac.uk/impactofsocialsciences/2015/10/22/does-
academia-edu-mean-open-access-is-becoming-
irrelevant/.
176. Hasani Mavriqi I, Sokolovska N, Ross-Hellhauer
T, Fecher B. 2020 Challenges in building
innovative, sustainable and open research
infrastructures. See https://zenodo.org/record/
3685642#.YJu1TrUzY2w.
177. Ficarra V, Fosci M, Chiarelli A, Kramer B,
Proudman V. 2020 Scoping the open science
infrastructure landscape in Europe. Zenodo.
See https://zenodo.org/record/4159838#.
YJzRjLUzY2w.
178. Ehls D. 2015 Diversity of participants in open
source projects comparing individual
demographics and participation rationales in
software, content, fun, and business
communities. In Open source innovation: the
phenomenon, participants behaviour, business
implications (eds C Herstatt, D Ehls), pp. 6280.
Oxford, UK: Routledge.
179. Vorvoreanu M, Zhang L, Huang Y-H, Hilderbrand
C, Steine-Hanson Z, Burnett M. 2019 From
gender biases to gender-inclusive design: an
empirical investigation. In Proc. 2019 CHI Conf.
on Human Factors in Computing Systems,
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
20
pp. 114. New York, NY: ACM. (doi:10.1145/
3290605.3300283)
180. Schimanski LA, Alperin JP. 2018 The evaluation
of scholarship in academic promotion and
tenure processes: past, present, and future.
F1000Research 7, 1605. (doi:10.12688/
f1000research.16493.1)
181. Herschberg C, Benschop Y, van den Brink M.
2018 Precarious postdocs: a comparative study
on recruitment and selection of early-career
researchers. Scand. J. Manag. 34, 303310.
(doi:10.1016/j.scaman.2018.10.001)
182. Hasselbring W, Carr L, Hettrick S, Packer H,
Tiropanis T. 2020 From FAIR research data
toward FAIR and open research software. It
Inf. Technol. 62,3947.
183. Bornmann L. 2017 Measuring impact in research
evaluations: a thorough discussion of methods
for, effects of and problems with impact
measurements. High Educ. 73, 775787.
(doi:10.1007/s10734-016-9995-x)
184. Rooyen Sv, Delamothe T, Evans SJW. 2010 Effect
on peer review of telling reviewers that their
signed reviews might be posted on the web:
randomised controlled trial. Br. Med. J. 341,
c5729. (doi:10.1136/bmj.c5729)
185. Bravo G, Grimaldo F, López-Iñesta E, Mehmani
B, Squazzoni F. 2019 The effect of publishing
peer review reports on referee behavior in five
scholarly journals. Nat. Commun. 10, 322.
(doi:10.1038/s41467-018-08250-2)
186. Ross-Hellauer T, Deppe A, Schmidt B. 2017
Survey on open peer review: attitudes and
experience amongst editors, authors and
reviewers. PLoS ONE 12, e0189311. (doi:10.
1371/journal.pone.0189311)
187. Hicks D, Wouters P, Waltman L, de Rijcke S,
Rafols I. 2015 Bibliometrics: the Leiden
manifesto for research metrics. Nat. News 520,
429. (doi:10.1038/520429a)
188. Curry S. 2018 Lets move beyond the rhetoric:
its time to change how we judge research.
Nature 554, 147. (doi:10.1038/d41586-018-
01642-w)
189. Fleck C. 2013 Impact factor fetishism. Eur. J.
Sociol. 2, 327356. (doi:10.1017/
S0003975613000167)
190. Priem J, Taraborelli D, Groth P, Neylon C. 2010
Altmetrics: a manifesto. See http://altmetrics.
org/manifesto.
191. Piwowar H. 2013 Introduction altmetrics: what,
why and where? Bull. Am. Soc. Inf. Sci. Technol.
39,89. (doi:10.1002/bult.2013.1720390404)
192. Wilsdon J, Bar-Ilan J, Frodeman R, Lex E, Peters
I, Wouters P. 2017 Next-generation metrics:
responsible metrics and evaluation for open
science. Report of the European Commission
Expert Group on Altmetrics. See http://www.
leibnizopen.de/suche/handle/document/147731.
193. Mounce R. 2013 Open access and altmetrics:
distinct but complementary. Bull. Am. Soc. Inf.
Sci. Technol. 39,1417. (doi:10.1002/bult.2013.
1720390406)
194. Mingers J, Leydesdorff L. 2015 A review of
theory and practice in scientometrics.
Eur. J. Oper. Res. 246,119. (doi:10.1016/j.ejor.
2015.04.002)
195. Hogan AM, Winter DC. 2017 Changing the rules
of the game: how do we measure success in
social media? Clin. Colon Rectal. Surg. 30,
259263. (doi:10.1055/s-0037-1604254)
196. Williams AE. 2017 Altmetrics: an overview and
evaluation. Online Inf. Rev. 41, 311317.
(doi:10.1108/OIR-10-2016-0294)
197. Momeni N, Rabbat M. 2016 Qualities and
inequalities in online social networks through
the lens of the generalized friendship paradox.
PLoS ONE 11, e0143633. (doi:10.1371/journal.
pone.0143633)
198. Pooladian A, Borrego Á. 2017 Methodological
issues in measuring citations in Wikipedia: a
case study in library and information science.
Scientometrics 113, 455464. (doi:10.1007/
s11192-017-2474-z)
199. Ghaly RS, Elabd E, Mostafa MA. 2016 Tweets
classification, hashtags suggestion and tweets
linking in social semantic web. In Proc. 2016 SAI
Computing Conf., SAI 2016, pp. 11401146. See
https://www.scopus.com/inward/record.uri?eid=
2-s2.0-84988826260&doi=10.1109%2fSAI.2016.
7556121&partnerID=40&md5=
509732ebda624255d3bc38dbd4558623.
200. Ryan K. 2016 Academic freedom and the eye of
power: the politics and poetics of open
enclosures. J. Polit. Power 9, 249268. (doi:10.
1080/2158379X.2016.1191162)
201. English PB, Richardson MJ, Garzon-Galvis C.
2018 From crowdsourcing to extreme citizen
science: participatory research for environmental
health. Annu. Rev. Public Health 39, 335350.
(doi:10.1146/annurev-publhealth-040617-
013702)
202. Allen BL. 2018 Strongly participatory
science and knowledge justice in an
environmentally contested region. Sci. Technol.
Hum. Values 43, 947971. (doi:10.1177/
0162243918758380)
203. Morales-Doyle D, Frausto A. 2020 Youth
participatory science: a grassroots science
curriculum framework. Educ. Action Res. 29,
6078. (doi:10.1080/09650792.2019.
1706598)
204. Timmermann C. 2019 Citizen science for
biomedical research and contributive justice.
Am. J. Bioeth. 19,6062. (doi:10.1080/
15265161.2019.1619875)
205. Saleh S, Sambakunsi H, Nyirenda D, Kumwenda
M, Mortimer K, Chinouya M. 2020 Participant
compensation in global health research: a case
study. Int. Health. 12, 524532. (doi:10.1093/
inthealth/ihaa064)
206. Kumar M. 2019 Championing equity,
empowerment, and transformational leadership
in (mental health) research partnerships:
aligning collaborative work with the global
development agenda. Front. Psychiatry 10, 99.
(doi:10.3389/fpsyt.2019.00099)
207. Godrie B, Boucher M, Bissonnette S, Chaput P,
Flores J, Dupere S, Gélineau L, Piron F, Bandini
A. 2020 Epistemic injustices and participatory
research: a research agenda at the crossroads of
university and community. Gatew.-
Int. J. Community Res. Engagem. 13, 6703.
208. OLeary H. 2018 Pluralizing science for inclusive
water governance: an engaged ethnographic
approach to WaSH data collection in Delhi,
India. Case Stud. Environ. 2,19. (doi:10.1525/
cse.2017.000810)
209. Burke BJ, Heynen N. 2014 Transforming
participatory science into socioecological praxis
valuing marginalized environmental
knowledges in the face of the neoliberalization
of nature and science. Environ. Soc.-Adv. Res. 5,
727. (doi:10.3167/ares.2014.050102)
210. Ottinger G. 2017 Making sense of citizen science:
stories as a hermeneutic resource. Energy Res. Soc.
Sci. 31,4149. (doi:10.1016/j.erss.2017.06.014)
211. Hendricks MD, Meyer MA, Gharaibeh NG, Van
Zandt S, Masterson J, Cooper JT, Horney JA,
Berke P. 2018 The development of a
participatory assessment technique for
infrastructure: neighborhood-level monitoring
towards sustainable infrastructure systems.
Sustain Cities Soc. 38, 265274. (doi:10.1016/j.
scs.2017.12.039)
212. Raphael C. 2019 Engaged communication
scholarship for environmental justice: a research
agenda. Environ. Commun. 13, 10871107.
(doi:10.1080/17524032.2019.1591478)
213. Mena CF, Arsel M, Pellegrini L, Orta-Martinez M,
Fajardo P, Chavez E, Guevara A, Espín P. 2020
Community-based monitoring of oil extraction:
lessons learned in the ecuadorian amazon. Soc.
Nat. Resour. 33, 406417. (doi:10.1080/
08941920.2019.1688441)
214. Holzmeyer C. 2019 Open science initiatives:
challenges for public health promotion. Health
Promot. Int. 34,624633. (doi:10.1093/heapro/
day002)
215. Kimura AH, Kinchy A. 2016 Citizen science:
probing the virtues and contexts of participatory
research. Engag. Sci. Technol. Soc. 2, 331361.
(doi:10.17351/ests2016.99)
216. Krings A, Kornberg D, Lane E. 2019 Organizing
under austerity: how residentsconcerns
became the flint water crisis. Crit. Sociol. 45,
583597. (doi:10.1177/0896920518757053)
217. Misonne D. 2020 The emergence of a right to
clean air: transforming European Union law
through litigation and citizen science. Rev. Eur.
Comp. Int. Environ. Law 30,3445. (doi:10.
1111/reel.12336)
218. Eymard L. 2020 From the French Citizens
Convention on Climate to the Conference on the
Future of Europe: a participatory science and
democracy perspective. Eur. Law J. 26,
136140. (doi:10.1111/eulj.12369)
219. Barzyk TM, Huang H, Williams R, Kaufman A,
Essoka J. 2018 Advice and frequently asked
questions (FAQs) for citizen-science
environmental health assessments.
Int. J. Environ. Res. Public Health. 15, 960.
(doi:10.3390/ijerph15050960)
220. Vercammen A, Park C, Goddard R, Lyons-White
J, Knight A. 2020 A reflection on the fair use of
unpaid work in conservation. Conserv. Soc. 18,
399404. (doi:10.4103/cs.cs_19_163)
221. Derrien MM, Zuidema C, Jovan S, Bidwell A,
Brinkley W, Lopez P, Barnhill R, Blahna DJ. 2020
Toward environmental justice in civic science:
youth performance and experience measuring
air pollution using moss as a bio-indicator in
industrial-adjacent neighborhoods.
Int. J. Environ. Res. Public Health. 17, 7278.
(doi:10.3390/ijerph17197278)
222. Prudic KL, Wilson JK, Toshack MC, Gerst KL,
Rosemartin A, Crimmins TM, Oliver JC. 2019
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
21
Creating the urban farmers almanac with
citizen science data. Insects 10, 294. (doi:10.
3390/insects10090294)
223. Herschan J, King R, Mkandawire T, Okurut K,
Lapworth DJ, Malcolm R, Pond K. 2020
The potential for citizen science to improve
the reach of sanitary inspections. Resources
9,119.
224. Oti IC, Gharaibeh NG, Hendricks MD, Meyer MA,
Van Zandt S, Masterson J, Horney JA, Berke P.
2019 Validity and reliability of drainage
infrastructure monitoring data obtained from
citizen scientists. J. Infrastruct. Syst. 25,
04019018.
225. Friedrich B, Reichel H, Renkert T. 2020 Citizen
science, the body of Christ, and testimonial
epistemology toward a distributed theology.
Ecum. Rev. 72, 223241. (doi:10.1111/erev.
12507)
226. Katapally TR. 2019 The SMART framework:
integration of citizen science, community-based
participatory research, and systems science for
population health science in the digital age.
JMIR MHealth UHealth 7, e14056. (doi:10.2196/
14056)
227. Bright J, De Sabbata S, Lee S. 2018
Geodemographic biases in crowdsourced
knowledge websites: do neighbours fill in the
blanks? Geojournal 83, 427440. (doi:10.1007/
s10708-017-9778-7)
228. Beattie R, Hippenmeyer S, Pauler FM. 2020
SCOPES: sparking curiosity through open-source
platforms in education and science. Front.
Educ. 5, 48. (doi:10.3389/feduc.2020.
00048)
229. Shelley-Egan C, Gjefsen MD, Nydal R. 2020
Consolidating RRI and Open Science:
understanding the potential for transformative
change. Life Sci. Soc. Policy 16, 7. (doi:10.1186/
s40504-020-00103-5)
230. Olesk A, Kaal E, Toom K. 2019 The possibilities
of Open Science for knowledge transfer in the
science-policy interface. J. Sci. Commun. 18,
A03. (doi:10.22323/2.18030203)
231. Willinsky J. 2003 Policymakersonline use of
academic research. Educ. Policy Anal. Arch.
11, 2. (doi:10.14507/epaa.v11n2.2003)
232. ElSabry E. 2017 Who needs access to research?
Exploring the societal impact of open access.
Rev. Fr. Sci. Linf. Commun. 11. (doi:10.4000/
rfsic.3271)
233. Okune A, Hillyer B, Albornoz D, Sambuli N, Chan
L. 2016 Tackling inequities in global scientific
power structures. See https://tspace.library.
utoronto.ca/handle/1807/71107.
234. Oliver K, Lorenc T, Innvær S. 2014 New
directions in evidence-based policy research: a
critical analysis of the literature. Health Res.
Policy Syst. 12,112. (doi:10.1186/1478-4505-
12-34)
235. Dodson EA, Geary NA, Brownson RC. 2015 State
legislatorssources and use of information:
bridging the gap between research and policy.
Health Educ. Res. 30, 840848.
236. Gold M. 2009 Pathways to the use of health
services research in policy. Health Serv. Res. 44,
11111136. (doi:10.1111/j.1475-6773.2009.
00958.x)
237. Boyko JA, Kothari A, Wathen CN. 2016 Moving
knowledge about family violence into public
health policy and practice: a mixed method
study of a deliberative dialogue. Health Res.
Policy Syst. 14, 31. (doi:10.1186/s12961-016-
0100-9)
238. Abekah-Nkrumah G, Issiaka S, Virgil L, Ermel J.
2018 A review of the process of knowledge
transfer and use of evidence in reproductive and
child health in Ghana. Health Res. Policy Syst.
16, 75. (doi:10.1186/s12961-018-0350-9)
239. Rowlands I, Nicholas D, Brown D, Williams P.
2011 Access to scholarly content: gaps and
barriers to access. Ser. Year 24, 123130.
240. Houghton J, Swan A, Brown S. 2011 Access to
research and technical information in Denmark.
Copenhagen, Denmark: Danish Agency for
Science, Technology and Innovation.
241. Houghton J, Rasmussen B, Sheehan P,
Oppenheim C, Morris A, Creaser C, Greenwood
H, Summers M, Gourlay A. 2009 Economic
implications of alternative scholarly publishing
models: exploring the costs and benefits. A
report to the Joint Information Systems
Committee (JISC). Loughborough, UK:
Loughborough University.
242. Jubb M. 2011 Heading for the open road: costs
and benefits of transitions in scholarly
communications. LIBER Q. 21, 102. (doi:10.
18352/lq.8010)
243. Beagrie N, Houghton J. 2012 Economic impact
evaluation of the economic and social data
service. London, UK: Charles Beagrie Ltd.
244. Beagrie N, Houghton J. 2014 The value and
impact of data sharing and curation. London,
UK: Charles Beagrie Ltd.
245. Beagrie N, Houghton J. 2016 The value and
impact of the European Bioinformatics Institute.
London, UK: Charles Beagrie Ltd.
246. Jones MM, Castle-Clarke S, Brooker D, Nason E,
Huzair F, Chataway J. 2014 The structural
genomics consortium: a knowledge platform for
drug discovery: a summary. Rand Health Q. 4,19.
247. McDonald D, Kelly U. 2012. The value and
benefits of text mining. Digital Infrastructure
Directions report into the value and benefits of
text mining to UK further and higher
education. Bristol, UK: JISC.
248. Parsons D, Willis D, Holland J. 2011. Benefits to
the private sector of open access to higher
education and scholarly research. Horsham, UK:
HOST Policy Research.
249. Leeson PD, St-Gallay SA. 2011 The influence of
theorganizational factoron compound quality
in drug discovery. Nat. Rev. Drug Discov. 10,
749765. (doi:10.1038/nrd3552)
250. Chalmers I, Glasziou P. 2009 Avoidable waste in
the production and reporting of research
evidence. Lancet 374,8689. (doi:10.1016/
S0140-6736(09)60329-9)
251. Houghton J, Sheehan P. 2006 The economic
impact of enhanced access to research findings.
CSES Working Paper.
252. Williams HL. 2013 Intellectual property rights
and innovation: evidence from the human
genome. J. Polit. Econ. 121,127. (doi:10.
1086/669706)
253. Arshad Z et al. 2016 Open access could
transform drug discovery: a case study of JQ1.
Expert. Opin. Drug Discov. 11, 321332. (doi:10.
1517/17460441.2016.1144587)
254. Johnson PA, Sieber R, Scassa T, Stephens M,
Robinson P. 2017 The cost (s) of geospatial
open data. Trans. GIS 21, 434445. (doi:10.
1111/tgis.12283)
255. Huber F, Wainwright T, Rentocchini F. 2020 Open
data for open innovation: managing absorptive
capacity in SMEs. R&D Manag. 50,3146.
256. Leonelli S. 2013 Why the current insistence on
open access to scientific data? Big data,
knowledge production, and the political
economy of contemporary biology. Bull. Sci.
Technol. Soc. 33,611. (doi:10.1177/
0270467613496768)
257. McGarity TO, Wagner WE. 2010 Bending science:
how special interests corrupt public health
research. Cambridge, MA: Harvard University Press.
258. Michaels D. 2008 Doubt is their product: how
industrys assault on science threatens your
health. Oxford, UK: Oxford University Press.
259. Oreskes N, Conway EM. 2010 Merchants of
doubt: how a handful of scientists obscured the
truth on issues from tobacco smoke to global
warming. New York, NY: Bloomsbury
Publishing.
260. Lundh A, Lexchin J, Mintzes B, Schroll JB,
Bero L. 2017 Industry sponsorship and
research outcome. Cochrane Database Syst.
Rev. 2, MR000033. (doi:10.1002/14651858.
MR000033.pub3)
261. Biddle JB. 2014 Can patents prohibit
research? On the social epistemology of
patenting and licensing in science. Stud. Hist.
Philos. Sci. A 45,1423. (doi:10.1016/j.shpsa.
2013.12.001)
262. Owen R, Macnaghten P, Stilgoe J. 2012
Responsible research and innovation: from
science in society to science for society, with
society. Sci. Public Policy 39, 751760. (doi:10.
1093/scipol/scs093)
263. von Schomberg R. 2019. Why responsible
innovation. In The international handbook on
responsible innovation: a global resource.
Cheltenham, UK: Edward Elgar Publishing.
264. Ross-Hellauer T, Reichmann S, Cole NL, Fessl A,
Klebel T, Pontika N. 2021 Dynamics of
cumulative advantage and threats to equity in
open science: a scoping review. Figshare.
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 211032
22
... Publishing in open access journals often involves higher publication costs, which makes it more difficult for institutes and universities from low-income countries to publish there [115]. Equity has been identified as a key aim of open science [116]. It is vital, therefore, that existing structural inequities in the scientific system are not unintentionally reinforced by open science practices. ...