Parole Officers’ Perspective on the Needs of Indigenous Offenders on Conditional Release
Bibliographic record
Abstract
Indigenous offenders are overrepresented in all aspects of the Canadian criminal justice system, including corrections. Indigenous offenders are overrepresented in the prison population and are least likely to be granted an early release from prison. Indigenous offenders also disproportionately breach their conditional release orders and consequently spend more time incarcerated than non-Indigenous offenders. To understand the reasons behind the breaches, need-based theoretical lenses were used to analyze data. Sixteen parole officers were interviewed using a narrative design where participants shared their accounts on supervising Indigenous offenders while highlighting the needs which they felt were not being met by their Indigenous clients. Maslow’s hierarchy of needs theory was used to understand the different levels of need, and upon analyzing data, eight themes emerged in the study. These themes reflected the unmet needs that parole officers identified which were putting Indigenous offenders at a higher risk to breach their conditional release orders. Recommendations include addressing systemic discrimination, helping Indigenous offenders gain social stability through resources that are accessible, the need to indigenize the criminal justice system through a change in policies and practice, adequate funding for stakeholders to provide support to Indigenous offenders, and the need for Indigenous offenders to heal. Adopting the outcomes of this research has implications for social change by reducing breaches and recidivism among Indigenous offenders in Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".