Popping the cultural bubble of violence risk assessment tools
Bibliographic record
Abstract
Violence risk instruments are administered in medico-legal contexts to estimate an individual’s likelihood of future violence. However, their ostensible limitations; in particular their mono-cultural and risk-centric composition, has drawn academic attention. These concerns may facilitate erroneous risk evaluations for certain non-white populations. Yet it remains unaddressed how cultural differences will be appraised in a risk assessment framework and which specific cultural factors should be considered. Provisions under the Canadian Criminal Code allow for Gladue Reports, to be sought by judicial officers prior to sentencing Indigenous people. Gladue Reports provide insights into an Indigenous person’s unique circumstances that may have led to their offending as well as community-based options for rehabilitation. We proffer that there may be value in augmenting the risk evaluation with culturally relevant Gladue style considerations identified by relevant Indigenous people to provide a more holistic account of an Indigenous individual’s circumstances.
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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 | 0.067 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".