Sentencing Vulnerability: An Empirical Study Into the Role of Personal Characteristics and the Foreseeable Experience of Confinement at the Sentencing of Older Adults
Bibliographic record
Abstract
This article presents and analyzes findings from a qualitative and quantitative review of reported Nova Scotia sentencing decisions (2013–2020) of aging individuals. The goal is twofold. First, by investigating the judicial discourse around personal characteristics at sentencing aging individuals, we specifically seek to understand where aging, and characteristics that interplay with aging, fit into current sentencing practices and the potential benefits and challenges of considering these factors. Secondly, and more generally, through the case study of older offenders, this article seeks to contribute to the largely theoretical scholarship that has engaged with the need for a methodical inclusion of a broader range of personal characteristics and experiences in sentencing. The review highlights a number of things. First, it shows that implementing a “characteristics and experience sensitive sentencing” (CESS) framework is possible because it is already occasionally used. Second, it shows that ignoring characteristics and experiences of offenders is not feasible; even if it was desirable to do so, these cannot always be ignored and they currently make their way into sentencing decisions in inconsistent ways, based on very different approaches, which, in turn, result in very different outcomes. Adopting a coherent approach to the use of personal characteristics and experiences is now a matter of bringing consistency in sentencing and a matter of promoting substantive over formal equality. Third, the study shows that sentencing decisions are filled with misconceptions about imprisonment and its impact on those sentenced. Some of the beliefs relied upon are not evidence-based, and yet they sometimes ground the sentencing decisions rendered. Implementing a CESS framework would require directly confronting and addressing these misconceptions. Fourth, the study highlights both some of the barriers to the implementation of a CESS framework and some possible solutions that would help overcome these barriers.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".