MétaCan
Menu
← Back to cohort
Record W3011264010 · doi:10.22215/etd/2016-11259

Exploring the Impact of the Segregation Intervention Initiative on Offender Outcomes

2016· dissertation· en· W3011264010 on OpenAlexaffabout
Emad Talisman

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsIntervention (counseling)InstitutionMental healthPsychologySample (material)PopulationClinical psychologyCriminologyPsychiatryMedicinePolitical scienceEnvironmental healthLaw

Abstract

fetched live from OpenAlex

Administrative Segregation (AS) is the correctional practice of removing offenders from the general inmate population, and relocating them to an isolated cell for up to 23 hours a day. This is done for the safety and security of the individual or the institution. There are concerns around the use of AS including its impact on mental health and the lack of access to services for offenders. The purpose of the Segregation Intervention (SI) is to help transition offenders out of AS and to change problem behaviours. The current study explored the impact of the SI with data drawn from a Canadian sample of offenders. SI participants (n = 292) were 2 times more likely to participate in and complete correctional programs within a 6-month follow-up period, compared to a matched group of non-participants (n = 292). SI participants were also 1.5 times more likely to be employed by the institutions.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.279
Threshold uncertainty score0.555

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.157
GPT teacher head0.408
Teacher spread0.251 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

Explore more

Same topicCriminal Justice and Corrections Analysis→French-language works237,207→