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Record W7106822445 · doi:10.14288/cjur.v7i2.196186

Early Prisoner Release Due to COVID-19: Can People’s Opinions be Changed?

2021· article· en· W7106822445 on OpenAlexaffabout

Bibliographic record

VenueOpen Collections · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsRed Deer Polytechnic
Fundersnot available
KeywordsIntervention (counseling)PrisonOpenness to experiencePunishment (psychology)Higher education

Abstract

fetched live from OpenAlex

Due to the high rates of infection and contagion of the COVID-19 virus in Canadian prisons, the Parole Board of Canada implemented new guidelines to facilitate the early release of low-risk prisoners to limit the spread of novel coronavirus. This study examines Canadians’ opinions regarding the early release of prisoners from correctional institutions due to COVID-19 and seeks to discover if psychoeducational intervention can positively alter these opinions. Additionally, level of education was examined to ascertain whether years of formal education had an effect on openness to change. Data from 289 Canadians found that psychoeducational intervention was an effective way to improve attitudes towards the early release of prisoners and that those with a higher level of education were not more affected by psychoeducational intervention; however, it was noted that those with a higher level of education expressed less concern overall regarding the increase of released offenders in the community.

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)0.005
metaresearch head score (Gemma)0.017
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.584

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.003
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 head0.052
GPT teacher head0.358
Teacher spread0.306 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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