Conference Agenda (All times are shown in Mountain Daylight Time (MDT) unless otherwise noted)

Undisclosed deviations between preregistration plans and published articles challenge research transparency. This study examines whether open science experts can aid in assessing such deviations by comparing their evaluations with those of psychology experts. Using content analysis, we compared assessments of 25 preregistration plans and articles, following a psychologist studying preregistration discrepancies framework. The 72.6% agreement rate suggests differences stemmed from distinct perspectives. Psychologists leveraged domain knowledge to scrutinize subtle inconsistencies, while open science experts, deeply committed to transparency principles, focused more on procedural adherence. Open science experts, unfamiliar with psychology's implicit norms, faced challenges in discerning field-specific knowledge and interpreting ambiguities as non-deviations due to unclear preregistration formats and ambiguous content. Targeted support and collaborative approaches engaging both expert groups could enhance preregistration adherence evaluations, balancing the open science experts' emphasis on transparent processes with the psychologists' domain expertise, ultimately strengthening research transparency and reproducibility.

Research teams from the industry, especially big technology companies, have been pushing impactful research work in the field of artificial intelligence (AI), changing the prospects of the field and the careers of many researchers. Research teams from big technology companies usually possess more data, bigger computing infrastructure, and research talent, granting them the advantages in advancing AI research. While most previous work focuses on investigating the advantages the industry has in the field of AI, and how their research publication is different from those published by academic teams, few research has been done to investigate whether working as an industry researcher is beneficial at the individual level. In this work, by analyzing co-authorship networks of researchers published in AI conferences, we investigate whether working in the industry gives researchers advantages in “intangible” forms, such as social capital, represented by the collaborative relationships they gained or maintained. Our result shows that the many advantages industry researchers possess correlate with the social capital they have, measured by degree centrality, eigenvector centrality, betweenness centrality, and effective size.

This paper describes how we developed and applied an exploratory critical friends orientation to a critical literature review that explores how critical feminist theories and approaches have been used in archival studies literature and reports on insights generated by this method. A critical friend is a trusted ally and critic who both values our ideas and can push them forward. Our “critical friends” critical literature review includes two parts; using traditional critical review methods we identify and synthesize how critical feminist approaches have been employed in archival studies literature. Atypically, and in part two, we also pay attention to those scholarly articles that discussed relevant or related concepts but were ultimately excluded from our final literature review corpus during the appraisal process. These peripheral articles act as critical friends to the research area under review. We describe how this approach identifies disciplinary boundaries and traditions and explores areas of overlap across intersecting and adjacent fields. A critical friends approach allows us to generously interpret and analyze the complex concepts of “feminisms” and “archives” across disciplinary fields in order to identify, learn from, and engage across fields that have much in common as well as fundamental differences.