Trust in the validity of published work is of fundamental importance to scientists. Confirmation of validity is more readily attained than addressing the question of whether fraud was involved. Suggestions are made for key stakeholders - institutions and companies, journals, and funders as to how they might enhance trust in science, both by accelerating the assessment of data validity and by segregating that effort from investigation of allegations of fraud.

Berg et al. (Games and Economic Behavior, 10, pp. 122-142, 1995) study trust and reciprocity in an investment setting. They find significant amounts of trust and reciprocity and conclude that trust is a guiding behavioral instinct (a “primitive” in their terminology). We modify the way information is presented to participants and, through a questionnaire, prompt strategic reasoning. To our surprise, none of our various treatments led to a reduction in the amount invested. Previously reported experimental results to the contrary did not survive replication. Our results suggest that those by Berg, Dickhaut, and McCabe are rather robust to changes in information presentation and strategic reasoning prompts. We discuss the implications of these findings.

Results of meta-analyses are of interest not only to researchers but often to policy-makers and other decision-makers (e.g., in education and medicine), and visualizations play an important role in communicating data and statistical evidence to the broader public. Therefore, the potential audience of meta-analytic visualizations is broad. However, the most common meta-analytic visualization – the forest plot – uses non-optimal design principles that do not align with data visualization best practices and relies on statistical knowledge and conventions not likely to be familiar to a broad audience. Previously, the Meta-Analytic Rain Cloud (MARC) plot has been shown to be an effective alternative to a forest plot when communicating the results of a small meta-analysis to education practitioners. However, the original MARC plot design was not well-suited for meta-analyses with large numbers of effect sizes as is common across the social sciences. This paper presents an extension of the MARC plot, intended for effective communication of moderate to large meta-analyses (k = 10, 20, 50, 100 studies). We discuss the design principles of the MARC plot, grounded in the data visualization and cognitive science literature. We then present the methods and results of a randomized survey experiment to evaluate the revised MARC plot in comparison to the original MARC plot, the forest plot, and a bar plot. We find that the revised MARC plot is more effective for communicating moderate to large meta-analyses to non-research audiences, offering a 0.30, 0.34, and 1.07 standard deviation improvement in chart users’ scores compared to the original MARC plot, forest plot, and bar plot, respectively.

We draw on a comprehensive dataset of metadata about journal articles substantively related to gender and politics published in 37 political science journals through the end of 2023, including Politics & Gender, to characterize and compare trends over time and the most prominent topics addressed by these journals. We show that the volume of work published by Politics & Gender has increased over time, has become increasingly quantitative in nature, and tends to focus on questions related to women running for political office and the nature of women’s political representation. These patterns closely reflect broader tendencies in the gender and politics research published by general-interest political science journals. Other journals dedicated to research on gender and politics tend to publish more qualitative research on topics including care work, the diffusion of equality norms, and conflict.