Louis Colnot’s scientific contributions

This paper addresses how to assign a monetary value to scientific publications, particularly in the case of multi-author papers arising from large-scale research collaborations. Contemporary science increasingly relies on extensive and varied collaborations to tackle global challenges in fields such as life sciences, climate science, energy, high-energy physics, astronomy, and many others. We argue that existing literature fails to address the collaborative nature of research by overlooking the relationship between coauthorship and scientists productivity. Using the Marginal Cost of Production (MCP) approach, we first highlight the methodological limitations of ignoring this relationship, then propose a generalised MCP model to value co-authorship. As a case study, we examine High-Energy Physics (HEP) collaborations at the Large Hadron Collider (LHC) at CERN, analysing approximately half a million scientific outputs by over 50,000 authors from 1990 to 2021. Our findings indicate that collaborative adjustments yield monetary valuations for subsets of highly collaborative papers up to 3 orders of magnitude higher than previous estimates, with elevated values correlating with high research quality. This study contributes to the literature on research output evaluation, addressing debates in science policy around assessing research performance and impact. Our methodology is applicable to authorship valuation both within academia and in large-scale scientific collaborations, fitting diverse research impact assessment frameworks or as self-standing procedure. Additionally, we discuss the conditions under which this method may complement survey-based approaches.

The European Union (EU), especially in the context of Cohesion Policy (CP), has played a crucial role in developing and promoting policy evaluation practices across its Member States. Evaluation systems across the Member States have been established to assess CP investments. Remarkably, the use of evaluation research and its contribution to stimulating policy learning has remained a “black box.” To address this issue, this article aims to develop a novel framework centered around four conditions for evaluation‐based policy learning, namely: (1) policy relevance, (2) resources and organizational settings, (3) quality of evaluation, and (4) evaluation culture. These conditions are retrieved from the existing literature on policy evaluation and applied to the six‐country cases across the EU. The findings suggest how loosening the formal EU evaluation requirements could affect policy learning in the Member States.