Corruption as a Form of Misconduct: Clarifying Concepts and Charting Ways Forward
Bibliographic record
Abstract
Corruption is one of the most pervasive threats to business environments and human rights around the world. Despite its prevalence and impact, management scholarship has yet to develop a mature field of research on the subject. The rapidly growing body of management research on organizational misconduct has begun to address corruption, but this growing sub-field is in need of both conceptual clarification as well as greater attention to the international organizational contexts where it is particularly problematic. This panel is designed to meet three goals: 1) demonstrate how scholarship on corruption makes distinct contributions to the large and growing literature on organizational misconduct; 2) demonstrate that to accurately characterize corruption dynamics, generate relevant theory, and design effective interventions, analyses are well-served by focusing attention on within and across organizational variation with deep attention to context; 3) build a more cohesive community of established and rising scholars whose research practice on corruption and organizational misconduct is characterized by deep engagement with organizational spaces, appropriately situated in their local field settings. We will address these issues with a combination of a panel discussion by experts and a facilitated Q&A session between panelists and the audience. Our panel includes presentations from organizational theorists who actively study corruption and organizational misconduct in both Western and non-Western contexts.
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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 | 0.067 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.017 | 0.130 |
| Scholarly communication | 0.049 | 0.098 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.017 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 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".