Risk in Action: the Practical Effects of the Youth Management Assessment
Bibliographic record
Abstract
This article illustrates the importance of empirical investigations that reveal `risk in action'. Using interviews, operation manuals and correctional policies, I examine the governance of female young offenders at `Youth House' (an open custody facility in Canada). This article focuses on the ways in which risk discourses and practices shape the governance process. Particular attention is paid to the discretionary power of front-line workers and administrators who employ the Youth Management Assessment (YMA), a risk tool used to govern young offenders. My research shows that contrary to the belief that risk tools remove the subjective nature of the governing process, such practices not only still exist but are necessary for risk tools to operate. Furthermore, I reveal an unanticipated outcome of risk tools. I argue their use unintentionally results in the surveillance of an unsuspecting population: those that govern. Risk tools are implemented seemingly with the intent to manage offenders, however, in practice the YMA also governs those that govern.
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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 | 0.049 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".