Artículos

Discussions about epistemic inequalities have for several years highlighted the need to engage critically and reflexively with the politics of citation. Many authors have called for colleagues to correct longstanding epistemic and material injustices by proactively citing scholars and scholarship from marginalised groups, thereby producing radical knowledge that disrupts power. Analysing the epistemic-political grammar of these calls, I note that they often assume that resistance and disruption are intrinsic to corrective citation – i.e. that citing names understood as marginal will by default undermine relations of power. But is that always the case? Drawing on three sets of empirical examples, I demonstrate that citation often does not have the epistemic and material effects we predict, or hope, it will, and may reinforce some inequalities at the same time as it disrupts others. I show that the effects of citation are complex and contingent because they are shaped by unpredictable interactions between different structures of power, unexpected (dis)connections between global and local inequalities, and dynamic relationships between injustice within texts and inequalities beyond them. I argue, therefore, that we must question the more binary and reifying logics of contemporary conceptualisations of citation and attempt to think about corrective citation differently. To contribute to this rethinking, I draw on several authors to propose an approach that celebrates the potential of corrective citation, but remains attentive to its limitations, foregrounding complexity and opacity, recognising the possible failures of radical epistemic practices, and probing our affective investments in them.

Psychosociology theories indicate that individual evaluation is integral to the recognition of professional activities. Building upon Christophe Dejours’ contributions, this recognition is influenced by two complementary judgments: the “utility” judgment from those in hierarchy and the “beauty” judgment from the peers. The aim of this paper is to elucidate how at INRAE individual assessment of scientists is conducted. This process follows a qualitative and multicriteria-based approach by peers, providing both appreciations and advice to the evaluated scientists (the “beauty” judgment). Furthermore, we expound on how INRAE regularly adapts this process to the evolving landscape of research practices, such as interdisciplinary collaboration or open science, assuring that assessments align with the current approaches of research activities.

The development of appropriate statistical models has lagged behind the ambitions of empirical studies analysing large scientific networks—systems of publications connected by citations and authorship. Extant research typically focuses on either paper citation networks or author collaboration networks. However, these networks involve both direct relationships, as well as broader dependencies between references linked by multiple citation paths. In this work, we extend recently developed relational hyperevent models to analyse networks characterized by complex dependencies across multiple network modes. We introduce new covariates to represent theoretically relevant and empirically plausible mixed-mode network configurations. This model specification allows testing hypotheses that recognize the polyadic nature of publication data, while accounting for multiple dependencies linking authors and references of current and prior papers. We implement the model using open-source software to analyse publicly available data on a large scientific network. Our findings reveal a tendency for subsets of papers to be cocited, indicating that the impact of these papers may be partly due to endogenous network processes. More broadly, the analysis shows that models accounting for both the hyperedge structure of publication events and the interconnections between authors and references significantly enhance our understanding of the mechanisms driving scientific production and impact.

A standard approach to compare research collaborations between pairs of countries is to look at the citations accrued by all publications with authors from both countries. This approach is often misleading, as aspects only marginally related to the collaboration between the country pairs may bias the result considerably. Among them, the main aspect is the number of co-authors. Publications with many co-authors have on average higher citation impact. If the mix of co-publications between two countries has a high share of such publications, the citation impact will likely be high. Moreover, publications with many co-authors tend to include many countries and are thus only to a limited extent characterising the actual collaboration between the selected pair of countries. The purpose of this study is to develop methods for comparisons of country pairs useful for policy makers, who use SciVal or similar tools. Five methods to compare international collaboration are developed and tested. It is noted that the standard approach for comparisons deviates the most. Fractional methods to calculate the citation impact are recommended, as they allow for the use of citations to all co-publications with a higher weight on the citations to publications in which the country pair dominates. As fractionalisation is laborious to carry out based on SciVal data, a more convenient option is also suggested, which is to use co-publications with maximum 10 co-authors. Elsevier should introduce better methods for comparisons of international collaborations and, until this has been made, help its users understand the limitations of the standard approach featured in SciVal. A by-product of the study is that international co-publications deliver a higher citation impact also when publications with the same number of co-authors are compared.