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ABSTRACT. Tens if not hundreds of thousands of scientists contribute to peer review by volunteering to be editors and reviewers of the millions of new research manuscripts submitted to scholarly journals every year. The diversity of these editors and reviewers abounds. They come from different national contexts with different personal and professional identities (Zuckerman and Merton 1971), disciplinary training and epistemological styles(Mallard, Lamont, and Guetzkow 2009), as well as varied experiences and statuses as members of the global scientific community. Yet, given this plurality of experience, identity, personal and professional interests and positions, how do scientists decide what’s quality science? And how do they justify their decisions? When they stake different epistemological positions on new works in their evaluations, whose opinion counts most for the final outcome? How, in short, do scientists’ ordinary and routine value judgements shape the advance of new works through peer review? In light of chronic concerns with reviewer reliability and bias in scientific evaluation (Bornmann, Mutz, and Daniel 2010), proposals to automate or augment parts of peer review with generative large language models (Lin et al. 2023), and rising public distrust and politicization of science (Druckman 2022), such questions concerning the centrality and social character of human judgment and uncertainty in science could not be timelier or more critical for sociologists to ask.

ABSTRACT. Peer review at elite journals disproportionately influences the scientific record, society, and the careers and composition of the scientific workforce. As a social process, peer review is susceptible to bias and idiosyncrasy, raising critical questions about fairness and equity, especially at elite venues. Yet, we lack an integrated understanding of such non-meritocratic influences on peer review across fields or at elite venues, because its confidential data are largely inaccessible, limiting reproducibility and theory building. In the uncommon event that access can be secured, or an experiment carried out, the data typically cannot be shared, precluding replication or effective synthesis of published findings, and rendering impossible analyses which would shed light on how editorial processes shape outcome inequalities, or how they vary across disciplinesor journal prestige.

ABSTRACT. As science grows more complex, the idea of assembling specialized researchers to pursue groundbreaking discoveries has gained momentum—similar to gathering a blacksmith, a mason, and a carpenter to mirror Leonardo da Vinci’s genius. The promise of joining complementary specialists fuels the proliferation of teams in a multitude of scientific domains, especially interdisciplinary ones. However, as the prevalence and size of teams have grown, science has not produced a surge of innovative work as we would expect. Recent research shows that adding more experts to a team does not always boost innovation. This raises a fundamental question: Does pooling the expertise of specialized scientists truly lead to scientific breakthroughs? To investigate, we analyze millions of scientific articles across the full spectrum of science to examine whether and how team members’ expertise impacts innovation. Our findings suggest that even as scientists increasingly specialize in their research fields, interdisciplinary teams of generalists consistently outperform those comprising specialists in making groundbreaking discoveries. In response to the increasing burden of knowledge, scientists often identify team members from complementary specialty areas and divide tasks accordingly to extend their reach. Our results will hopefully prompt science policymakers to revisit the utility of this approach. To meet grand challenges and spur innovative work, it may be more fruitful to foster the “intra-personal interdisciplinarity” of individual scientists and the joining of such broad-thinking generalists into teams.

ABSTRACT. Scholars have questioned the ability of social scientists to separate the self from the subject. Nowhere is this more evident than in the practice of “mesearch,” when researchers study a dimension of the social order on which they are marginalized. To better understand the influence of mesearch, we draw on original survey data from 2,850 sociologists linked with their publications in the Web of Science to ask two questions: What is the role of traditionally marginalized groups in creating knowledge about sociodemographic minority groups in sociology? How is mesearch rewarded or penalized within sociology? To study mesearch, we compare the text of paper abstracts with the self-identified demographics of a paper’s authors across not only gender and race, but also sexuality, disability, and family background, which are typically unavailable in bibliometric analyses.

ABSTRACT. The global scientific landscape emerges from a complex interplay of collaboration and competition, where nations vie for dominance while simultaneously fostering the diffusion of knowledge on a global scale. This raises crucial questions: What underlying patterns govern international scientific recognition and influence? How does this structure impact knowledge dissemination? Traditional models view the global scientific ecosystem through a core-periphery lens, with Western nations dominating knowledge production. Here, we investigate the dynamics of international scientific recognition through the lens of national preferences, introducing a novel signed measure to characterize national citation preferences and enabling a network analysis of international scientific recognition. We find that scientific recognition is related to cultural and political factors, as well as economic strength and scientific quality. Our analysis challenges the conventional core-periphery narrative, uncovering instead several communities of international knowledge production that are rapidly fragmenting the scientific recognition ecosystem. Moreover, we provide compelling evidence that this network significantly constrains the diffusion of ideas across international borders. The resulting network framework for global scientific recognition sheds light on the barriers and opportunities for collaboration, innovation, and the equitable recognition of scientific advancements, with significant consequences for policymakers seeking to foster inclusive and impactful international scientific endeavours.

ABSTRACT. The conventional peer review process for reviewing grant proposals is known to be biased against riskier proposals.1-3 As such, the metascience community has proposed many alternate approaches to evaluating grant proposals that could improve science funding outcomes.4 One such approach was proposed by Chiara Franzoni and Paula Stephan in a paper on how expected utility — a formal quantitative measure of predicted success and impact — could be a better metric for assessing the risk and reward profile of science proposals.5 Inspired by their paper, the Federation of American Scientists (FAS) proposed to implement expected utility in peer review through forecasting and conducted a pilot study of the proposed approach.6 In this paper, we share the results of that pilot and its implications for future implementation of expected utility forecasting in science funding review.