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In many academic fields, the number of papers published each year has increased significantly over time. Policy measures aim to increase the quantity of scientists, research funding, and scientific output, which is measured by the number of papers produced. These quantitative metrics determine the career trajectories of scholars and evaluations of academic departments, institutions, and nations. Whether and how these increases in the numbers of scientists and papers translate into advances in knowledge is unclear, however. Here, we first lay out a theoretical argument for why too many papers published each year in a field can lead to stagnation rather than advance. The deluge of new papers may deprive reviewers and readers the cognitive slack required to fully recognize and understand novel ideas. Competition among many new ideas may prevent the gradual accumulation of focused attention on a promising new idea. Then, we show data supporting the predictions of this theory. When the number of papers published per year in a scientific field grows large, citations flow disproportionately to already well-cited papers; the list of most-cited papers ossifies; new papers are unlikely to ever become highly cited, and when they do, it is not through a gradual, cumulative process of attention gathering; and newly published papers become unlikely to disrupt existing work. These findings suggest that the progress of large scientific fields may be slowed, trapped in existing canon. Policy measures shifting how scientific work is produced, disseminated, consumed, and rewarded may be called for to push fields into new, more fertile areas of study.

The credibility revolution (sometimes referred to as the “replicability crisis”) in psychology has brought about many changes in the standards by which psychological science is evaluated. These changes include (a) greater emphasis on transparency and openness, (b) a move toward preregistration of research, (c) more direct-replication studies, and (d) higher standards for the quality and quantity of evidence needed to make strong scientific claims. What are the implications of these changes for productivity, creativity, and progress in psychological science? These questions can and should be studied empirically, and I present my predictions here. The productivity of individual researchers is likely to decline, although some changes (e.g., greater collaboration, data sharing) may mitigate this effect. The effects of these changes on creativity are likely to be mixed: Researchers will be less likely to pursue risky questions; more likely to use a broad range of methods, designs, and populations; and less free to define their own best practices and standards of evidence. Finally, the rate of scientific progress—the most important shared goal of scientists—is likely to increase as a result of these changes, although one’s subjective experience of making progress will likely become rarer.

After having received little attention over the past decades, one of the least known human rights—the right to enjoy the benefits of scientific progress and its applications—has had its dust blown off. Although included in the Universal Declaration of Human Rights (UDHR) and in the International Covenant on Economic, Social and Cultural Rights (ICESCR)—be it at the very end of both instruments -this right hardly received any attention from States, UN bodies and programmes and academics. The role of science in societies and its benefits and potential danger were discussed in various international fora, but hardly ever in a human rights context. Nowadays, within a world that is increasingly turning to science and technology for solutions to persistent socio-economic and development problems, the human dimension of science also receives increased attention, including the human right to enjoy the benefits of scientific progress and its applications. This contribution analyses the possible legal obligations of States in relation to the right to enjoy the benefits of scientific progress and its applications, in particular as regards health.

This paper explores the integration of machine learning into economics and social sciences, assessing its potential impact and limitations. It introduces fundamental machine learning concepts and principles, highlighting the differences between the two disciplines, particularly the focus on causal inference in economics and prediction in machine learning. The paper discusses diverse applications of machine learning, from extracting insights from unstructured data to creating novel indicators and improving predictive accuracy, while also addressing challenges related to data quality, computational efficiency, and data ownership. It emphasizes the importance of standardization, transparency, and ethical considerations in prediction tasks, recognizing that machine learning is a powerful tool but cannot replace economic theory. Ultimately, researchers remain optimistic about the transformative potential of machine learning in re-shaping research methodologies and generating new insights in economics and social sciences.

Issue Title: Festschrift on Richard T. De George

ABSTRACT BACKGROUND This paper extends the findings of the Cochrane systematic review of audit and feedback on professional practice to explore the estimate of effect over time and examine whether new trials have added to knowledge regarding how optimize the effectiveness of audit and feedback. METHODS We searched the Cochrane Central Register of Controlled Trials, MEDLINE, and EMBASE for randomized trials of audit and feedback compared to usual care, with objectively measured outcomes assessing compliance with intended professional practice. Two reviewers independently screened articles and abstracted variables related to the intervention, the context, and trial methodology. The median absolute risk difference in compliance with intended professional practice was determined for each study, and adjusted for baseline performance. The effect size across studies was recalculated as studies were added to the cumulative analysis. Meta-regressions were conducted for studies published up to 2002, 2006, and 2010 in which characteristics of the intervention, the recipients, and trial risk of bias were tested as predictors of effect size. RESULTS Of the 140 randomized clinical trials (RCTs) included in the Cochrane review, 98 comparisons from 62 studies met the criteria for inclusion. The cumulative analysis indicated that the effect size became stable in 2003 after 51 comparisons from 30 trials. Cumulative meta-regressions suggested new trials are contributing little further information regarding the impact of common effect modifiers. Feedback appears most effective when: delivered by a supervisor or respected colleague; presented frequently; featuring both specific goals and action-plans; aiming to decrease the targeted behavior; baseline performance is lower; and recipients are non-physicians. DISCUSSION There is substantial evidence that audit and feedback can effectively improve quality of care, but little evidence of progress in the field. There are opportunity costs for patients, providers, and health care systems when investigators test quality improvement interventions that do not build upon, or contribute toward, extant knowledge.

Research on perfectionism is flourishing, but the unspecified distinction between perfectionism and the pursuit of excellence is a lingering issue that urgently needs conceptual, theoretical, and empirical attention. In this article, excellence and perfection are defined as distinct goals that form the basis of two different but related constructs. To move this idea forward, the term excellencism is introduced. Perfectionism and excellencism are defined and their similarities and differences are illustrated using symbolic logic and adjectives from the English lexicon. A point is made to clearly indicate that excellencism is a required reference point for reassessing the healthiness or unhealthiness of personal standards perfectionism. Using the law of diminishing returns as an analogy, a theory-driven rationale is proposed, and three alternative hypotheses are formulated. Showing that personal standards perfectionism is associated with better, comparable, and worse outcomes compared with excellencism offers the needed and sufficient conditions for respectively supporting the hypothesis that perfectionism is a healthy, unneeded, or deleterious pursuit. The propositions advanced in this theoretical article are more than incremental, and their practical implications are far-reaching: If personal standards perfectionism yields no added value or deleterious outcomes over and above excellencism, then excellence rather than perfection should be promoted.

It appears paradoxical that science is producing outstanding new results and theories at a rapid rate at the same time that researchers are identifying serious problems in the practice of science that cause many reports to be irreproducible and invalid. Certainly, the practice of science needs to be improved, and scientists are now pursuing this goal. However, in this perspective, we argue that this seeming paradox is not new, has always been part of the way science works, and likely will remain so. We first introduce the paradox. We then review a wide range of challenges that appear to make scientific success difficult. Next, we describe the factors that make science work—in the past, present, and presumably also in the future. We then suggest that remedies for the present practice of science need to be applied selectively so as not to slow progress and illustrate with a few examples. We conclude with arguments that communication of science needs to emphasize not just problems but the enormous successes and benefits that science has brought and is now bringing to all elements of modern society.

We develop a model describing how false paradigms may persist, hindering scientific progress. The model features two paradigms, one describing reality better than the other. Tenured scientists display homophily: They favor tenure candidates who adhere to their paradigm. As in statistics, power is the probability (absent any bias) of denying tenure to scientists adhering to the false paradigm. The model shows that because of homophily, when power is low, the false paradigm may prevail. Then, only an increase in power can ignite convergence to the true paradigm. Historical case studies suggest that low power comes either from lack of empirical evidence or from reluctance to base tenure decisions on available evidence.

According to the standard no miracles argument, science’s predictive success is best explained by the approximate truth of its theories. In contemporary science, however, machine learning systems, such as AlphaFold2, are also remarkably predictively successful. Thus, we might ask what best explains such successes. Might these AIs accurately represent critical aspects of their targets in the world? And if so, does a variant of the no miracles argument apply to these AIs? We argue for an affirmative answer to these questions. We conclude that if the standard no miracles argument is sound, an AI-specific no miracles argument is also sound.