Response to Commentary: Cross-Examining Risk “Knowledge”
Bibliographic record
Abstract
Proportionality, as stipulated in the 2003 Youth Criminal Justice Act (YCJA), sets out the parameters within which we examine the implications of embedding assessments within youth pre-sentence reports (PSRs). Our article raises questions for academic and legal professionals and, ultimately, judges, who may or may not elect to consider risk/need assessments in the determination of a sentence. The sentencing principles contained in section 38(2) stipulate that (c) the sentence must be proportionate to the of the offence and the of of the young person for that offence; and that (e) subject to [the aforementioned] paragraph (c), the sentence must (ii) be the one that is most likely to rehabilitate the young person and reintegrate him or her into society. (emphasis added) The YCJA is unequivocal in the priority afforded to proportionality. By contrast, rehabilitation, albeit important, is of less relevance to sentencing. We do not dispute the importance or impact of treatment and rehabilitative approaches in the management of offenders. Our concern is with whether the use of risk-assessment information sentencing contravenes the principle of proportionality and/or disparity. The seriousness of the the of harm caused, and the youth's degree of responsibility are central to proportionality, but not to the assessment of risk. Risk assessments are designed to identify factors and criminogenic needs that can be targeted through treatment in an effort to prevent, or more accurately, to minimize a predicted of re-offence. Risk introduces a future-oriented model, wherein punishment is determined on the calculation of scores and the predicted likelihood that the accused will recidivate. Risk scores may well be able to identify a group of offenders who are more likely to recidivate, but not everyone in the group will with certainty do so. Prediction of recidivism is not definitive. There is no absolute certainty that an offender will re-offend, or re-offend with the same of seriousness. In fact, our research indicates that assessments are more likely to target those most likely to breach conditions, a relatively minor criminal offence, and are less reliable in predicting who will commit a serious violent offence. Further, not all of the risk-assessment practices used in Canada rely on empirically tested tools (Hannah-Moffat and Maurutto 2003). The prediction of in the medical context is quite distinct from in the criminal-justice sphere. In the medical context, where patients have the choice to determine their course of treatment, pre-emptive interventions may be the optimal course of action, even though there is no certainty of disease. By contrast, in the legal system, such pre-emptive intervention would contravene the YCJA, as well as our collective rights as set out in the Canadian Charter of Rights and Freedoms. Both sets of legislation restrict the ability of the courts to impose punishment on the anticipation of a threat or on the basis of an offender's criminogenic needs. Moreover, information can result in more punitive dispositions for those youth exhibiting greater needs. Marginalized youth, whose lives tend to be mired in a range of criminogenic and other needs, are more likely to score as high-risk. As a result, they are at risk of receiving longer custodial sentences and/or a greater number of conditions attached to their disposition, making them more vulnerable to breach, increased surveillance, and further criminalization. Risk assessments are not as transparent or as objective as presumed. Risk assessments include a range of discretionary and arbitrary decisions that are obscured in the presentation of PSRs (cf., Rose 1998; Maurutto and Hannah-Moffat 2006). Crowns, defence and duty counsel, judges, and many writers of PSRs are rarely attuned to how far subjective judgements frame PSRs and, consequently, rarely contest this information. …
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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 | 0.009 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.092 | 0.076 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".