Bibliographic record
Abstract
Is the civic duty to report crime and corruption a genuine moral duty? After clarifying the nature of the duty, I consider a couple of negative answers to the question, and turn to an attractive and commonly held view, according to which this civic duty is a genuine moral duty. On this view, crime and corruption threaten political stability, and citizens have a moral duty to report crime and corruption to the government in order to help the government’s law enforcement efforts. The resulting duty is triply general in that it applies to everyone, everywhere, and covers all criminal and corrupt activity. In this paper, I challenge the general scope of this argument. I argue that that the civic duty to report crime and corruption to the authorities is much narrower than the government claims and people might think, for it only arises when the state (i) condemns genuine wrongdoing and serious ethical offenses as “crime” and “corruption,” and (ii) constitutes a dependable “disclosure recipient,” showing the will and power to hold wrongdoers accountable. I further defend a robust duty to directly report to the public—one that is weightier and wider than people usually assume. When condition (ii) fails to obtain, I submit, citizens are released of the duty to report crime and corruption to the authorities, but are bound to report to the public, even when the denunciation targets the government and is risky or illegal.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| 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".