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Record W3126818402

An Exploration of Justice Personnel's Perceptions of and Experiences with Mentally Ill Offenders

2020· article· en· W3126818402 on OpenAlexaboutno aff
Kailee Oates

Bibliographic record

VenueSFU Undergraduate Research Symposium Journal · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsCriminal justiceFraming (construction)CriminalizationCriminologyPrisonPsychologyMental illnessPerceptionDemonizationStigma (botany)Economic JusticeMental healthPsychiatryPolitical scienceLawPolitics
DOInot available

Abstract

fetched live from OpenAlex

Deinstitutionalization and increased criminalization of mentally ill offenders result in their overrepresentation within the criminal justice system. Justice personnel now encounter mentally ill offenders more frequently. Yet, their workplace experiences with and perceptions of mentally ill offenders are largely misunderstood. Six semi-structured interviews with justice personnel from Western Canada were conducted. Inductive coding generated three preliminary themes. “It’s all about funding,” centers on staff shortages, inadequate prison services, fragmented community supports, and a lack of affordable housing. “If it bleeds, it leads” identifies negative media framing that contributes to mental illness stigma and police demonization, which increases public fear and disdain of mentally ill offenders more broadly. Finally, “They’re just people,” suggests the perceptions and experiences of justice personnel are predominantly positive. These findings shed light on the lived realities of justice personnel and are an integral first step to informing policy, improving service delivery, and identifying programming needs.

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 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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.121
GPT teacher head0.408
Teacher spread0.287 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2020
Admission routes1
Has abstractyes

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