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Record W2058914941 · doi:10.7202/1024294ar

The Civic Duty to Report Crime and Corruption

2014· article· en· W2058914941 on OpenAlexvenueno aff
Candice Delmas

Bibliographic record

VenueLes ateliers de l éthique · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsDutyWrongdoingLanguage changeArgument (complex analysis)Political scienceGovernment (linguistics)LawEnforcementLaw enforcementLaw and economicsSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.293
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2014
Admission routes1
Has abstractyes

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