<i>Special Section On Ethics in Management Research:</i>Norms, Identity, and Community in the 21st Century
Bibliographic record
Abstract
Growth in research on management has been accompanied by awareness of ethical problems that pose a serious threat to the integrity of our publication process, and the soundness of our knowledge base. This Special Section in AMLE analyzes the forces that give rise to research practices that violate espoused research norms and presents remedies that can curtail these practices. In this opening article, we review key points raised by the articles in this Special Section, but also explore some of them in greater depth. We open with a discussion of how escalating competition for scarce publication space is shaping ethical choices, creating an environment in which many researchers believe that the playing field is tilted against them. We then examine how growth exacerbates competitive pressures, leading to weakening of community cohesion. This in turn undermines research norms, with adverse impact on professional identity. Our attention next turns to the ethical challenges confronting editors and reviewers. We argue that these gatekeepers also experience pressures that constrain their ability to oversee the publication process diligently and fairly. We conclude with a summary of the four articles that make up the Special Section.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".