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Record W2314400839 · doi:10.1177/1043986201017002003

Specific Deterrence and Sentence Length

2001· article· en· W2314400839 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Contemporary Criminal Justice · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of AlbertaUniversity of Winnipeg
Fundersnot available
KeywordsRecidivismDeterrence (psychology)SentenceDrunk driversDrunk drivingConfoundingPsychologyDriving under the influencePrisonSample (material)CriminologyPoison controlStatisticsInjury preventionComputer scienceMathematicsMedicineMedical emergencyArtificial intelligence

Abstract

fetched live from OpenAlex

Researchers have assessed the effect of longer prison sentences by conducting aggregate-level studies of general deterrence. However, relatively little attention has been paid to the specific deterrent effects of longer custody sentences on individual offenders. The authors evaluated the effect of sentence length on drunk driving recidivism by using official records in a retrospective research design. A sample of 514 incarcerated drunk drivers we are followed up for 24 to 45 months in Alberta, Canada. The study searched for possible sentencing thresholds, the optimum sentence length at which point deterrent effects are maximized, and used multivariate statistical analysis to control for possible confounding background variables. The authors observed that sentence length exerted consistent deterrent effects on repeat drunk driving, even for chronic offenders. Shorter sentences were less effective in discouraging drunk driving recidivism, while sentences longer than 6 months did not produce additional benefits.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.072
GPT teacher head0.330
Teacher spread0.258 · 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