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UID:pretalx-sips2025-budapest-RM9KJS@pretalx.com
DTSTART;TZID=CET:20250626T100000
DTEND;TZID=CET:20250626T100700
DESCRIPTION:This paper is part of replication studies that I4R of Ottawa Un
 iversity coordinated in different events for replication games. This study
  evaluates the effectiveness of varying levels of human and artificial int
 elligence (AI) integration in reproducibility assessments of quantitative 
 social science research. We computationally reproduced quantitative result
 s from published articles in the social sciences with 288 researchers\, ra
 ndomly assigned to 103 teams across three groups — human-only teams\, hu
 man teams with AI assistance (“cyborg”)\, and AI teams ("machine"). Fi
 ndings reveal that most “cyborg” and “human” teams completed repro
 ducibility checks (91% and 94%\, respectively)\, while only 37% of “mach
 ine” teams successfully reproduced findings\, highlighting challenges fo
 r automated only reproduction. The “human” teams detected statisticall
 y significant coding errors compared to the other groups. 94% of teams pro
 posed at least one quality robustness check\, with “human” and “cybo
 rg” teams performing better. These results underscore the strengths and 
 limitations of AI assistance reproduction.
DTSTAMP:20260512T130255Z
LOCATION:Second floor 213
SUMMARY:LT26(prerecorded): Comparison of Human\, AI-Assisted\, and Quasi-Au
 tomated Approaches to Assessing Research Reproducibility in Quantitative S
 ocial Science - Belay W Weldemicheal
URL:https://pretalx.com/sips2025-budapest/talk/RM9KJS/
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