MétaCan
Menu
Back to cohort
Record W2152811671 · doi:10.1177/0887403412462234

The Impact of Aggravating and Mitigating Factors on the Sentence Severity of Sex Offenders

2012· article· en· W2152811671 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

VenueCriminal Justice Policy Review · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAggravating FactorSentencePsychologyContext (archaeology)Sex offenderCriminal justiceCriminologyMedicine

Abstract

fetched live from OpenAlex

The aggravating and mitigating circumstances that contribute to increased, or decreased, sentence severity for sex offenders have largely been unexplored. Although previous studies have evaluated offending groups who have targeted adult-only, or children-only victims, the current study compares the sentencing outcomes of both offending groups. Using a sample of 519 federally sentenced sex offenders in the province of Quebec the current study explores the extent to which the Canadian criminal justice system penalizes offender- and offense-based characteristics. The results indicate that offense-based characteristics increased sentence severity for offenders who victimized adults and offender-based characteristics influenced sentence severity for offenders who victimized children. Findings are discussed within the context of previous studies to empirically explore sex offender sentencing and compare differences that aggravating and mitigating circumstances have on sentence outcomes.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.663
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.102
GPT teacher head0.412
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