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Solving the Battle of First-Authorship:
Using Interactive Technology to
Highlight Contributions
Abstract
Human-Computer Interaction research is traditionally
collaborative. However, the current authorship model –
i.e., placing authors’ names in a particular order –
makes the contributions of collaborators who are not
the “first author” (or not mentioned) less visible which
negatively affects career paths. Still, if smaller and
larger contributions are equally rewarded with a “good”
position in the author list, a researcher’s achievements
may be overrated. We suggest a solution with
interactive technology to highlight contributions. The
benefits include high visibility of contributions, in-situ
access to in-depth researcher profiles, in situ access to
similar work by the contributors, and low incentive for
artificial credits.1
Author Keywords
Authorship; first author; contributor model; visualized
contributor model; contribution; interactive technology.
1 This paper has been written in a word by word collaboration,
and the order of authorship does not reflect differential
contributions. An “artificial” first author has been introduced,
indicating the collaborative efforts: AC BD indicates the initials
of the co-authors first names (Afsaneh and Christine) and last
names (Bauer and Doryab). CMU is the abbreviation of
Carnegie Mellon University and UzK refers to the University of
Cologne.
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CHI'16 Extended Abstracts, May 7–12, 2016, San Jose, CA, USA.
Copyright is held by the owner/author(s). Publication rights licensed to ACM.
ACM 978-1-4503-4082-3 /16/05...$15.00.
http://dx.doi.org/10.1145/2851581.2892582
AC BD 1
CMU UzK
Pittsburgh + Cologne, United
States + Germany
cmuuzk@gmail.com
Christine Bauer
University of Cologne
50969 Cologne, Germany
bauer@wim.uni-koeln.de
Afsaneh Doryab
Carnegie Mellon University
Pittsburgh, PA 15213, USA
adoryab@cs.cmu.edu
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ACM Classification Keywords
H.5.m. Information interfaces and presentation (e.g.,
HCI): Miscellaneous.
Introduction
Human-Computer Interaction (HCI) research is
characterized by collaboration and is frequently of
interdisciplinary nature. Often several researchers with
different backgrounds contribute to a project. In the
current publishing practice that puts a list of authors in
a particular order (i.e., “author list” authorship model),
some contributions are hidden one or the other way. It
is impossible to put all the contributors as the first
author. This is particularly challenging when everybody
contributes equally to the research; it results in what
we call “artificial ranking”.
A proposed solution to overcome these problems is the
“contributor model” of authorship, which is a means for
transparently indicating specific roles of contributors in
a research and manuscript writing project [1]. This
model has been discussed in various disciplines such as
medicine [3, 4], engineering [2], and the social
sciences [5]; but the model is still on a more
conceptual level and has not been operationalized
through technology. In this paper, we propose to use
interaction technology, which is developed and widely
used in the HCI community, to operationalize the
contributor model.
Contributions of this paper are as following:
An honest discussion of the challenges inherent
in the current authorship model (i.e., an
ordered list of authors), and describing the
consequences of this model on collaboration,
motivation, and career opportunities.
An interactive design based on the stakeholder
perspectives that promises a viable solution for
revealing the visibility of contributions.
Challenges With the Current Practice of
Authorship in HCI
Collaborative research in HCI typically unites different
disciplines and skills. Especially in big, interdisciplinary
projects, a large group of people is involved in the
research, with each person making distinct
contributions. When it comes to the dissemination of
results, two main scenarios exist: (1) Either all
participants of the research project collaborate in
writing a manuscript, or (2) only a subgroup of
researchers engage in the writing process. As
Borenstein & Shamoo [1] point out, “[W]riting is not
the paramount component of every researcher’s job,
but the published paper is the main vehicle for
communicating research findings to colleagues and the
broader world”. In other words, a work may be worth
publishing only if the group contributes to the research
endeavor and writing is not the only way to contribute
to a publication.
We identify two major challenges inherent to the
“author-ordered model”: It is not transparent, and it
has a strong impact on researcher’s motivation to
contribute.
Lack of Transparency
A main challenge in the currently applied model of
authorship is the lack of transparency concerning the
content and importance of authors’ respective
contributions. Borenstein & Shamoo [1] point out that
Afsaneh Doryab’s contributions:
Idea generator
Idea conceptualization
Scenario development
Visualization
Writing
Coordination
0 papers related to authorship
1 paper related to interactive
technologies
Bewell: A smartphone
application to monitor, model
and promote wellbeing
4 papers related to collaboration:
Context-aware information
adaptation in collaborative
settings
Activity-aware
recommendation for
collaborative work in operating
rooms
…
Christine Bauer and Afsaneh Doryab
other co-authored papers: 0
Christine Bauer’s contributions:
Idea conceptualization
Scenario development
Literature search
Writing
0 papers related to authorship
2 papers related to interactive
technologies:
Active Listening” to Instant
Messaging and E-mail:
Benefits and Limitations
…
2 papers related to collaboration:
Collaboration by Location-
based Crowdsourcing
…
Arthur Small: Copy editing
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in the current model, the “author list on a manuscript is
often used as a proxy for determining who made which
type of contribution to a project. Yet, decoding what
the list means and who even actually wrote the
relevant manuscript is quite difficult” [1].
If there is a list of authors, it is not clear who, for
instance, had the idea, who designed and implemented
the machine learning process, and who documented the
research for publication. It is almost necessary to be an
“insider” to a particular research field to know how to
interpret a particular author list [1]. For example, in a
field such as in mobile health, an interdisciplinary team
of researchers with backgrounds in health, software
engineering, machine learning, psychology, and social
science would be necessary to perform a
comprehensive and strong study. The bare author-
order list fails to convey the important nuance of these
differentiated contributions.
Similarly, if a paper, for example, introduces a mobile
application to collect data, includes some machine
learning analysis on the data, and also has a qualitative
section with interviews and qualitative coding, it is
difficult to create an ordered list of authors that would
reflect who and what is most important in the paper, as
the paper only exists in its “entirety/collaboration”. Not
to mention one of the researchers as an author or to
credit participating researchers as second, third or later
author would mean not to equally recognize/appreciate
the contribution of all of the team members.
Furthermore, a limitation of the current authorship
system is that it creates inequality and invisibility of
contributions. On the one hand, some readers just look
for the more “famous” names (usually an advisor or
senior researcher) and do not recognize the additional
efforts of other researchers involved. On the other
hand, some readers actually pay attention to the
authors next to the more famous one, recognizing
those as the “newcomers” who deserve attention. The
latter might be one of the motifs behind recruiting
“guest authors” (a guest author is a person who does
not contribute but is given credit as author (e.g., [7])
to get better recognition oneself.
Damaging Effect on the Motivation to Contribute
As Borenstein & Shamoo [1] point out, decoding the
order in an author list is quite difficult, because
research teams decide on their “author list strategy”
themselves – or it is imposed on the team by one of the
senior researchers in the team. For example, some
advisors favor to put the names in an alphabetic order.
Others might decide to put the PhD student who needs
to graduate first. Others might decide to identify the
first author before the work has actually started. Others
let the work be done and then decide who writes the
paper and should be the first author.
In some cases, people also compromise on the order of
authors. Compromising prevents fights and conflicts;
yet, it has long-term consequences on career paths
(e.g., the number of one’s first authored papers is
frequently used as recruitment criterion for research
positions) and may create an uncomfortable working
atmosphere (e.g., emotional stress). In some cases, a
compromise on the order of authors negatively affects
the spirit of collaboration and reduces motivation for
the rest of the team to contribute as much as they
would for a paper where they would be the first author
[6].
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The contribution of some researchers may be so vital
that without it no paper would exist. Yet, when such
key contributions are buried through less prominent
placement in authorship rankings, it affects the
motivation and effort to make a stronger contribution.
Contributor Model as a Viable Solution
One possible solution to address the mentioned
challenges is the contributor model of authorship. The
contributor model [9] was suggested as a solution to
the problem of the ill-defined authorship. While
different variations of the contributor model exist (e.g.,
[8], [10]), the unifying theme among them is the goal
of revealing the respective contributions of multiple
authors, thereby “creating a more meaningful
opportunity to delineate each person’s range of tasks”
[1].
Applying this contributor model solves various
challenges:
Strong contributors are identified by the quantity and
significance of their contribution, not by the order
their names appear in the author list.
Contributions are systematically visible: there is no
need for negotiation and authorship policies.
Authorship can go beyond the writing task;
everybody who has contributed to the research can
and should be credited. For example, if a research
assistant built the system that was evaluated, he or
she is listed as contributor even if they did not
participate in the writing task.
The tension and competition that result from the
order system are reduced. Most people would not
care about the order as long as their contributions
are visible.
Accountability for each team member increases. If
the contribution of someone cannot be visualized,
then it is also no contribution.
While the contributor model does certainly not resolve
all authorship-related problems [1], it has many
benefits over the current authorship system.
Notwithstanding its theoretical benefits, the discussion
on possible implementation approaches has raised
many questions (e.g., how to leverage tools that are
familiar to non-tech end users or who will gather the
required information and how) [5] but has not provided
any concrete solutions. We contribute to closing this
gap by suggesting a solution that implements the
contributor model with interactive technologies and
recommendation algorithms.
Our Approach
Our approach is to pick up and extend the contributor
model with links where contributions for a paper are
clearly described and visualized. In addition, our model
visualizes an automatically collected summary of
contributions and research record per author, across
several work.
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Figu
r
As ca
n
appr
o
r
e 1: Our approa
c
n
be seen in Figu
r
ach. For every p
a
c
h to implement t
h
r
e 1, we take an i
a
per, related pap
e
h
e visual contribu
nterlinked
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rs to the topic
tor model.
are show
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literature
d
n
; this is a featur
e
databases have a
e
that many scien
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lready implemen
t
t
ific
t
ed. For
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each contributor on a paper, the respective
contributions (e.g., methods, idea generator, statistics)
in the respective paper are given. In addition, our
approach anticipates listing related papers by the
specified contributor/author using recommendation
techniques. This feature has the benefit of emphasizing
each author’s research expertise and making this
information easily available. Furthermore, for the
specific parts of a paper, similar papers are listed. This
is different from the related work in the reference list.
In Figure 1, we exemplify this by providing a list of
other papers that use the same method as the current
paper. This feature will give the readers an in situ
access to similar application of this method.
Stakeholders and Their Benefits
Several stakeholders may benefit from this interactivity
model (Figure 1); the major benefits are summarized in
Table 1 and detailed below.
1. Assessor (assessing work quality or profile)
a) Recruiters can more easily and transparently
assess the qualification of the person through
their work and publications. For example, the
search committee for a faculty position can go
to the candidate’s website and find the
publications. When they hover over the name
of the person on each paper, they see her/his
contributions and similar papers to this one, list
of collaborators, and overall weight of
contribution in each area in case the work is
interdisciplinary. For example, in the HCI
community people use different methods from
other domains. Not everyone has the same
background and knowledge, but they get
together to do research, and everybody
contributes according to her/his expertise. At
the same time they learn something from other
domains and over time they become experts in
those disciplines as well. This expertise
development is highlighted in form of weights
in the researcher’s profile when viewed in each
publication.
b) Reviewers can more easily and transparently
assess the quality of someone’s research
(papers and projects)
c) Ethics committee or editor can assess the real
authorship (giving credit/fairness)
d) Program committee members of a conference
can find the right reviewers to assess other
people’s work (identifying and then assessing
the candidates). For example, a PC member is
trying to find the right reviewer for a CHI paper
in Meta-Analysis, and he currently does not
know any candidates capable of reviewing the
paper. His search on research papers in Meta-
Analysis in Google Scholar return a large list of
papers. He hovers the mouse on the name of
each author and sees a brief list of
contributions that particular person has made
in that paper. He also sees the author’s
research background and her/his similar
papers to this one. As such, the search space
and the amount of time looking for the right
person to do the task will decrease, as the PC
member only needs to get access to one of the
review candidates’ papers to get a sense of
their level of expertise in Meta-Analysis.
2. Authors themselves can have a record of their
work/contributions as proof or evidence for later
presentation. It helps their visibility and reduces
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the urge for active communication of research
activities and highlighting their expertise and
qualifications.
Stakeholder
Benefits of the visual contributor model
Assessor - Recruiters or search committee can
more easily and transparently assess the
qualification of the person through their
work and publication
- Reviewers and ethics committee can
assess the quality of someone’s work and
real authorship
- Program committee members can
effortlessly find the right reviewers to
assess other people’s work
Author - A record of one’s work/contribution
history as proof or evidence for later
presentation
- Visibility
- Reducing the urge for active
communication of research activities and
qualifications
Owner/
fundraiser
Having evidence of selling and
communication skills
Reader - Contacting the right person
- Finding the right thread to continue the
search
Table 1: Stakeholders and their benefits of the visual
contributor model.
3. Owner/fundraisers who make the research a reality
through their effort will have evidence on each
publication on their abilities to sell and
communicate research.
4. A reader interested to learn more about a specific
topic can easily identify the appropriate contributor
for potential further reading or outreach.
Application Scenarios
Table 2 lists the application scenarios where the
contributor model can address some of the issues in the
current author-ordered model. In scenarios, where one
author is the main contributor, the interactive
contributor model is similar to the traditional authorship
model. However, it double emphasizes the
contributions of this dominant contributor, quantifies
the amount of contribution, and provides details about
what has been done. As a result, the interactive
contributor model makes the contributions more
transparent in this scenario. For the readers, it takes
less effort to engage in further search for relevant
papers, as the model shows the authors’ similar work
by hovering on the names. At the same time, the
model emphasizes the weight and impact of the
contribution a person has made to the paper. For
example, if the person has many papers in that
particular topic, then s/he is likely to have expertise in
this field.
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Table 2. Application scenarios
Case Challenge with the author-ordered model Interactive contributor model
One dominant
contributor
With order the “dominant” contribution is not really
visible (whether 90% or just 70%)
The first author is the main contributor maybe the same way as it is now,
but it double emphasizes her/his contributions, quantifies the amount of
contribution, and gives details about what has been done which makes it
more transparent and effortless for the readers for further investigation
Equal contributors Impossible to distinguish the roles and – above all – the
order of contributions
- Showing authors’ similar work by hovering on the name emphasizes the
weight and impact of the contribution this person has made to the paper. For
example, if the person has many papers in that particular topic, then s/he
must have expertise.
- Showing similar work of the authors will mostly help the readers to find
related work of the same topic/same researchers/same expertise without
having to actively search for it. Existing strategies show similar papers to
this topic, but they do not show any similar people who do this kind of work.
The contribution of other
authors is so vital that
without it no paper would
exist
Impossible to make those vital contributions visible on
the paper (the important 10%)
Showing authors similar work by hovering on the name emphasizes the
weight and impact of the contribution this person has made to the paper. For
example, if the person has many papers in that particular topic, then s/he
must have expertise.
Joint work, but only one
is doing the writing task
No clear distinction between contributor/author
(definition gap)
- Makes everybody’s roles and skills visible
e.g., the ability to get funding
Significant contribution
but not the main concern
of the ‘study’
No clear distinction between contributor/author
(definition gap)
- Showing similar work of the authors will mostly help the readers to find
related work of the same topic/same researchers/same expertise without
having to actively search for it. Existing strategies show similar papers to
this topic but they don’t show any similar people who do this kind of work.
- Makes everybody’s roles and skills visible
e.g., the ability to get funding
No/little contribution by
a person
- Possible to have the role of coffeemaker
- Putting the novice Ph.D. students to motivate them
- Inviting guest authors to make their name look good
- Ghostwriting (with money and without)
- Outsourcing (e.g. statistics)
- One person has to do the work, but another person is
the author (the first person is not mentioned as author)
- It does not completely inhibit the possibility of invited authors but it
increases the transparency
- Captures the ghostwriters and identifying the holes in the contribution. For
example, if the paper is heavily statistical but none of the author's
background/ related papers/contributions are about statistical methods, then
it will raise the question: who has done the statistics?
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Showing similar work of the contributors will mostly
help the readers to find related work of the same
topic/same researchers/same expertise without having
to actively search for it. Existing strategies show similar
papers to a paper’s topic, but currently they do not
show any similar people who do this kind of work.
This approach however, does not address the issue of
invited/prestigious/forced authors; still, by
adding/visualizing each author’s contribution to this
particular paper, it helps clarification. Although it does
not completely prevent the possibility of invited and
ghostwriters, but it increases the transparency. In fact,
mentioning roles could be beneficial and prestigious.
For example, highlighting one’s role in a research paper
as ‘idea generator’ or ‘fundraiser’ may have a major
impact on future career/research opportunities that
otherwise would not be visible by only
looking/searching for the name of an author of the
paper.
The proposed model captures the ghostwriters and
identifies the holes in the contribution. For example, if
the paper is heavily statistical but none of the author's
background/related papers/contributions are about
statistical methods, then it will raise the question: who
has done the statistics?
Discussion
The proposed contributor model can address the main
issues of the current author-ordered model and add
more transparency and fairness to the authorship
model. However, a number of concerns might arise in
applying this model, which need to be discussed:
The contributor model does not completely solve
the first-authorship problem. While the ‘traditional’
system requires authors to agree on the order of
authors, the contributor model requires agreement on
contributions, and that might lead to even more
discussions among collaborators. We, however, argue
that this situation can actually be an opportunity for the
people involved in the research to highlight their
contributions that would otherwise be hidden. It also
motivates the collaborators to be aware of their roles
and to make visible contributions. As mentioned before,
if a contribution cannot be visualized, it is no
contribution.
When discussing authorship/contributions, it is
important to consider the citation method as well. As
papers are generally referred to by the first author’s
name, it may happen that the first mentioned name will
(still) be perceived as the most ‘prominent’ one, which
could weaken the benefits of the contributor model.
Further investigations are necessary to deal with this
issue. However, a simple solution could be to use a
random/signature name in the first-author place, e.g. a
combination of initials derived from the authors’ names.
We have shown this in our paper by placing our initials
in the first author’s place followed by the alphabetic
order of our last names.
The contributor model can also be gamed in the
same way as the current authorship model. The
concern is whether the ghostwriting role may end in a
‘ghost-contributor role’, where someone claims
contributions they did not make. While this might be
the case, we still think that discussion about the
contributions and listing them explicitly will narrow
down the problem because many (or at least some) will
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shy away from getting credit for tasks they did not
perform.
The interactivity will only be useful in the online
version and the issues will remain in the printed
version. The solutions for how to represent our
approach in the printed documents are yet to be
explored. However, as most papers are first viewed
online and the printed version is rarely used, we
consider this issue to be of minor importance. The main
idea is to make the contributions visible to the
stakeholders, and the interactive online version of the
paper fulfills this purpose.
Conclusion
We believe research and tools developed by HCI
researchers can offer a practical solution to the first-
authorship battle that almost all researchers in any
discipline face at some point in their career. In this
paper, we took the first step to introduce the benefits
of using interaction technology and recommendation
techniques to highlight the contributions of
collaborating researchers instead of letting the author-
ordered list implicate the weight of contributions. Our
goal is to motivate the HCI researchers to actively
participate in changing the traditional perspectives on
research collaboration and paper writing, and to help
develop a transparent system for presenting
collaborative research accomplishments without the
need for negotiation, conflict, or compromise.
References
1. Jason Borenstein, Adil E. Shamoo. 2015.
Rethinking Authorship in the Era of Collaborative
Research. Accountability in Research 22, 5: 267-
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http://dx.doi.org/10.1080/08989621.2014.968277
2. Barry Bozeman, Jan Youtie. 2015. Trouble in
Paradise: Problems in Academic Research Co-
authoring. Science and Engineering Ethics: 1-27.
http://dx.doi.org/10.1007/s11948-015-9722-5
3. Erol Digusto. 1994. Equity in authorship: a strategy
for assigning credit when publishing. Soc Sci Med
38, 1: 55-58.
4. Mahsa Ghajarzadeh, Mehdi Mohammadifar, Saeid
Safari. 2013. How to Define an Author?: Awareness
of Authorship Criteria. Anesth Pain 3, 1: 226-227.
http://dx.doi.org/10.5812/aapm.10877
5. IWCSA Report (2012). Report on the International
Workshop on Contributorship and Scholarly
Attribution. Harvard University and the Wellcome
Trust. Retrieved January 13, 2016 from
http://projects.iq.harvard.edu/attribution_worksho
p
6. Hanna Krasnova, Kerstin Schäfer, Oliver Günther,
Ola Henfridsson, Natasha Veltri. 2012. Publication
strategy for junior researchers: quantity vs.
quality, the first authorship and the optimal
number of authors. ECIS 2012 Proceedings. Paper
86. Retrieved January 13, 2016 from
http://aisel.aisnet.org/ecis2012/86
7. Stuart F. Quan. 2008. Guests and Ghosts Begone:
Guest Authorship and Ghostwriting in the Journal of
Clinical Sleep Medicine. Journal of Clinical Sleep
Medicine 4, 3.
8. Drummond Rennie, Veronica Yank, Linda L.
Emanuel. 1997. When authorship fails: a proposal
to make contributors accountable. JAMA 278, 7:
579-85.
9. David B. Resnik. 1997. A proposal for a new
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The authors present a nice sketch of a tool that
supports the contributor model for research papers.
The main thought of the paper is that the contributor
model, if applied solely to a paper, probably cannot
fully reveal its benefits. However, in combination with
an interactive tool that enables the reader to perceive
a paper not as an isolated document but as a piece of
research linked to other works, the contributor model
can reveal its power as it becomes clear (and in a
way proven), for example, who the experts are in a
certain field.
Let us focus on the main point again: existing
interactive tools have drawbacks because role models
are missing. At the same time, the contributor model
has limitations if not used in a network of papers.
In my opinion, the paper at hand not only designs a
system that supports the contributor model but also
has the potential to empower the contributor model
to become a well-perceived value add for
researchers. In my opinion, the authors should more
clearly express that only the combination of both the
contributor model and an interactive tool can provide
this extraordinary value, for example, in terms of
transparency of the contributions.
The main idea and new perspective that I get out of
the paper is that the contributor model and
interactive tools can be seen as complementary
assets.
However, I miss some discussions:
1.) Inferring on contributions: “Showing authors’
similar work by hovering on the name emphasizes
the weight and impact of the contribution this person
has made to the paper. For example, if the person
has many papers in that particular topic, then s/he
must have expertise.” This is just an assumption.
There are some risks associated with this approach.
For example, the contributions of senior scholars who
are identified as experts by this approach might be
overestimated.
2.) What idea of humans do we have in mind when
designing such a tool? In some ways, I see that the
idea of the insidious researcher, “the ghostwriter,”
etc., could be revealed by the tool, or contributions
could be put into question: “Who has done the
statistics?” The contributor model itself is a model of
trust. As scientists, we generally trust each other that
the things we declare are true to the best of our
knowledge, just like we trust each other about the
data we have and the results we obtain from it.
However, the tool presented seems to give rise to a
control model. This issue should be carefully
discussed: are researchers put under general
suspicion? Do we perform a shift in trust using this
approach?
I enjoyed reading this paper and found it interesting
to view the contributor model and interactive tools as
complementary assets. Thanks for sharing this nice
idea!
Commentary
For alt.chi paper
Solving the Battle of First-
Authorship: Using Interactive
Technology to Highlight
Contributions
Stefan Hirschmeier
University of Cologne
Pohligstr. 1
50672 Köln, Germany
hirschmeier@wim.uni-koeln.de
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I think this paper addresses an important issue and
proposes an interesting solution, with several
possible benefits: helping authors clarify their
contributions for readers and reviewers, framing a
particular publication in authors’ previous work,
discouraging ghost authorship. This system could be
put in place by incorporating it into article templates,
maybe at CHI, for example.
What is missing, I think, is a reflection on how the
current list style actually serves the interests of
authors, or at least some of them, and how authors
may therefore resist the proposed template:
1) Research contributions may seem clear for each
and every one of us authors in a collective, but they
may become contested once we try to spell them out
clearly. This may be less the case when roles are
strictly disciplinary, and more when people with
similar expertise collaborate with some - but not
obvious - differences. Attempts to clarify
contributions may introduce tension and resentment
among co-authors. Ambiguity often has a
peacekeeping function in relationships.
2) As for ghostwriters, I fear that there will be
countless formulations at hand to specify a
contribution that was largely symbolic or totally
absent. I trust people’s creativity to overcome a
request for transparency.
3) The ordered author list is a social game whose
rules, costs and benefits are clearly understood by
most authors, at least after a couple publications. The
specified contribution author list would also become
such a social game, if it gets widely implemented.
People who play the system now will likely play the
system then, formulating contributions to reflect their
group’s hierarchies, differential interests, and
interactional balances.
4) If contributions were to be quantified as
percentages, as the authors discuss at page 8, for
example, this could become a Pandora’s box. Now
that we think about it, it is sort of a mystery that the
author list has remained un-quantified, given the
metric frenzy that increasingly dominates the
scholarly arena. The very process of creating these
quantifications could then reinforce inequalities and
write them in stone, as numbers are persistent and
become incorporated into indexes and so on
(alongside with what Merton identified as the
Matthew effect in science, discussed in Robert K.
Merton, The Matthew Effect in Science, Science, 159:
3810, pp. 56-63). Thus, I think that we should resist
the idea of quantifying co-authors’ contributions.
Commentary
For alt.chi paper
Solving the Battle of First-
Authorship: Using Interactive
Technology to Highlight
Contributions
Cosima Rughiniș
University of Bucharest
Schitu Măgureanu 9
Bucharest, Romania
cosima.rughinis@sas.unibuc.ro
alt.chi: Authorship and Reviews
#chi4good, CHI 2016, San Jose, CA, USA
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