Subverting and Negotiating Risk Assessment: A Case Study of the LSI in a Canadian Youth Custody Facility
Bibliographic record
Abstract
This case study examines probation officers and local agents' strategies and approaches to completing and applying the Level of Supervision Inventory (LSI) to female young offenders at Youth House, an open-custody facility in central Canada. Both the probation officers and Youth House staff express concerns about the LSI and employ strategies to subvert its almost deterministic impact through discretionary practices. I show that the LSI's risk calculations are subverted by both Youth House staff and, to a lesser extent, the probation officers, thereby reducing the LSI's importance in the management of offenders. Lastly, I find that probation officers utilize the LSI to externalize responsibility. This tool provides them with a means by which to justify and rationalize their management decisions. This study supports not only the growing body of literature that examines risk in practice but also contributes to increasing knowledge on how risk rationales on the macro-level translate to practices at the local level.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".