Editor-in-Chief's introduction to issue 41(2).

Abstract

The editorial team is pleased to release the second issue of volume 41 of Politics and the Life Sciences. This issue adds six articles to the journal ’ s quickly growing list of open access articles. It features five articles selected with a competitive call for proposals for research to be funded by the Association for Politics and the Life Sciences (APLS) on the broad topic of “ Life Science in Politics: Methodological Innovations and Political Issues. ” The call solicited proposals for registered reports that use “ life science theory and methods to study political phenomena and the study of the intersection of science and political attitudes. ” The resulting articles address health data sharing behavior, attitudes toward environmental justice, links between physical formidability and views on the climate, racial and ethnic variation in negativity bias, and interoceptive sensitivity and political ideology. The guest editors provide an incisive overview and synthesis of the articles in their “ Introduction ” (Friesen et al., 2022). These articles continue the journal ’ s and APLS ’ s commitment to the promotion of rigorous scientific practices through registered reports (RRs). RRs require researchers to clearly state their hypotheses and detail their analysis plans before beginning data collection and completing their manuscript. In addition, they require journal editors to accept a submitted RR for publication or reject it prior to knowing the results. The objective of this approach is to minimize bias in researchers, who face myriad consequential decisions during the research process, and in journal editors, who want the positive attention for their journals that comes with the publication of “ novel ” and “ significant ” results. Credible issues for consideration have been raised about registered reports, including in this journal (McDermott, 2022). But mounting evidence suggests that compared to non-RR research, RRs are resulting in significantly fewer supported hypotheses (i.e., more null and negative results), greater computational reproducibility (i.e., improved replication of quantitative results), and greater perceived article quality (i.e., better methodological rigor and overall quality) (Chambers & Tzavella, 2022).