Reflections on Scientific Misconduct in Management: Unfortunate Incidents or a Normative Crisis?
Bibliographic record
Abstract
Taking as our starting point Merton’s (1942/1973) defense of science facing pressures from totalitarian regimes, we argue that today’s challenge to the integrity of management scholarship does not come primarily from external demands for ideological conformity, but from escalating competition for publication space in leading journals that is changing the internal dynamics of our community. We invited nine scholars from different countries and with different backgrounds and career trajectories to provide their brief views of this argument. Following an introduction that summarizes the argument, we present their different reactions by dividing and introducing the work into those who took a broad field-level perspective, those with a more macro view, and those who suggested possible remedies to our dilemmas. In conclusion, we note that questionable research practices, retractions, and highly publicized cases of academic misconduct may irreparably damage the legitimacy of our scholarship unless the management research community airs these issues and takes steps to address this challenge.
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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 | 0.137 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.032 | 0.068 |
| Scholarly communication | 0.033 | 0.025 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.032 | 0.043 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".