Done But Not Published: The Dissertation Journeys of Roy J. Lewicki and J. Keith Murnighan
Bibliographic record
Abstract
Abstract This article explores the tumultuous path to publication that begins for many of us with trying to publish our dissertation. We invited Roy J. Lewicki and J. Keith Murnighan—the 2013 and 2015 recipients of the International Association for Conflict Management (IACM) Lifetime Achievement Award—to reflect on this process, as neither of them were successful in getting their dissertation articles published. We also asked them to reflect on the twists and turns of academic publishing, and we asked Max Bazerman to integrate these reflections. Together, we hope to spark generative conversations that will enable scholars to successfully navigate their academic careers.
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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 | 0.074 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.029 |
| Scholarly communication | 0.042 | 0.025 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".