Mitigating ingroup bias in regulatory firms: The role of inspector professionalism
Bibliographic record
Abstract
Abstract This article adopts the lens of ingroup bias to study why regulatory firms tasked with enforcing regulatory compliance may underperform in their duties. We theorize that ingroup bias can lead regulatory agents to grant unwarranted trust to ingroup clients with whom they share salient characteristics, resulting in less stringent inspections for these clients compared to outgroup clients. We further examine how this effect is moderated by inspectors' professionalism, a human capital dimension reflecting an individual's engagement with their profession and internalization of its norms and standards. Using a difference‐in‐differences approach on micro‐data tracking 86 inspectors across 24,650 inspections of 462 vessels at a marine inspection firm, we find compelling evidence of ingroup bias and show that inspectors' professionalism mitigates its impact on regulatory enforcement stringency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".