Collaboration Initiative for Doctoral Students to Conduct, Publish, and Learn from Replications
Bibliographic record
Abstract
Replication studies are increasingly recognized as essential for building evidence-based management theory. This symposium will introduce doctoral students and management scholars to an emerging novel way to become involved in designing, executing, and publishing multi-study constructive replications in top journals. It outlines how the Advancement of Replications Initiative in Management (ARIM; www.arimweb.org) coordinates the joint execution and publication of replication studies by teams of doctoral students, faculty mentors, and replication-minded scholars from different universities every year. Brief presentations, a panel discussion, and a Q&A segment will create a platform for symposium participants to discuss related opportunities and challenges of replication research. How replications contribute to the production of scientific knowledge Author: Xavier Martin; Tilburg University How to design, justify, and publish replication studies Author: William Obenauer; University of Maine ARIM initiative as collective big-team replication projects for doctoral students Author: Andreas Schwab; Iowa State University Introduction of 2025 ARIM collective doctoral student replication program Author: Christopher Castille; Nicholls State University
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.501 | 0.635 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.007 | 0.040 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.042 | 0.029 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".