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Record W2730428114 · doi:10.35502/jcswb.40

Taking the Pulse: perceptions of crime trends and community safety and support for crime control methods in the Canadian Prairies

2017· article· en· W2730428114 on OpenAlexaffvenueabout
Ian V. McPhail, Mark E. Olver, Carolyn Brooks

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPunishment (psychology)Punitive damagesPreferenceCrime preventionPerceptionPsychologyFear of crimeCriminologySocial psychologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

The present study analyzed crime survey data extracted from the 2012 Saskatchewan Taking the Pulse survey on a sample of 1,700 adult Saskatchewan residents. The focus was on examining perceptions of crime trends, perceived effectiveness of various methods for controlling crime, and their sociodemographic correlates. The majority of survey respondents perceived crime in general to be on the rise (37%) or to have not changed at all (48%) over the last three years. Individuals who perceived crime to have decreased were significantly more likely to support alternatives to punishment as effective methods for reducing crime, while individuals who perceived crime to be on the rise were twice as likely to support the use of punitive methods. Perceptions of community safety were unrelated to preference for one crime reduction method over another. Education level was inversely related to crime trend perceptions (r = -.14) and preference for punitive methods to reduce crime (r = -.20). Finally, the results of logistic regression indicated higher levels of education, higher income, and perceptions of crime decreasing were all uniquely associated with a preference for alternatives to punishment in reducing crime. In these analyses, younger age was predictive of a preference for alternatives in reducing youth crime, while urban residential setting was associated with a preference for alternatives to punishment in reducing crime in general.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.425
Teacher spread0.359 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2017
Admission routes3
Has abstractyes

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