Inserting professionals and professional organizations in studies of wrongdoing: The nature, antecedents and consequences of professional misconduct
Bibliographic record
Abstract
Professional misconduct has become seemingly ubiquitous in recent decades. However, to date there has been little sustained effort to theorize the phenomenon of professional misconduct, how this relates to professional organizations, and how this may contribute to broader patterns of corruption and wrongdoing. In response to this gap, in this contribution we discuss the theoretical and empirical implications of analyses that focus on the nature, antecedents and consequences of professional misconduct. In particular, we discuss how the nature of professional misconduct can be quite variegated and nuanced, how boundaries between and within professions can be either too weak or too strong and lead to professional misconduct, and how the consequences of professional misconduct can be less straightforward than normally assumed. We also illuminate how some important questions about professional misconduct are still pending, including: how we define its different organizational forms; how it is instigated by the changing nature of professional boundaries; and how its consequences are responded to in professional organizations and society more widely.
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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 | 0.039 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".