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Record W2121700730 · doi:10.6000/1929-4409.2012.01.15

The Effect of Perceived Deterrence on Compliance with Authorities: The Moderating Influence of Procedural Justice

2012· article· en· W2121700730 on OpenAlexvenueno aff
Peter Verboon, Marius van Dijke

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsCompliance (psychology)Deterrence (psychology)Procedural justiceDeterrence theoryEconomic JusticeElement (criminal law)CriminologyOrder (exchange)Social psychologyPolitical sciencePsychologyBusinessLaw and economicsLawSociologyPerception

Abstract

fetched live from OpenAlex

In order to stimulate compliance, authorities often use deterrence instruments. However, scientific literature from the fields of criminology, sociology and psychology has not been consistent in when or why deterrence is effective in shaping compliance. In the present study we investigated the role of procedural justice in relation to deterrence. Procedural justice has strong effects on people’s attitudes and behaviour regarding the social collective, including compliance with authorities. We argued that particularly authorities who are considered procedurally fair are successful in stimulating compliance with the use of deterrence instruments. In support of these ideas, a field survey in which we focused on sanction severity as the first element of deterrence and an experiment in which we focused on detection probability as the second element of deterrence revealed that procedural justice and deterrence instruments interactively strengthen each other’s effect in promoting compliance. These finding may partly explain the sometimes-contradictory results from prior work about the effectiveness of deterrence by supporting a justice perspective on the effectiveness of deterrence in increasing compliance with authorities.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.101
GPT teacher head0.411
Teacher spread0.310 · 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 designObservational
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

Citations17
Published2012
Admission routes1
Has abstractyes

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