Bibliographic record
Abstract
What would a jurisprudence of sentencing that was induced from the experience of punishment, rather than deduced from the technocracy of criminal justice, look like? Rather than focusing narrowly on the question of quantum, such a jurisprudence would be concerned with the character and quality of punishment. A fit sentence would account for pain, loss, estrangement, alienation, and other features of the offender’s aggregate experience of suffering at the hands of the state in response to his or her wrongdoing. This would be a broader, more resolutely political conception of criminal punishment. This article shows that the jurisprudence of the Supreme Court of Canada has nudged the law in precisely this direction, calling on judges to think about sentencing in ways better attuned to the lived experience of punishment. In judgments concerning police misconduct, collateral consequences of a sentence, and delayed parole, the Court has recognized the salience of pain and hope to the task of sentencing, firmly establishing that proportionality – the guiding measure of a fit sentence – is an indelibly individualized concept that must be calibrated to the real effects of the criminal process and proposed sentence on the life lived by the offender. With this, we can begin to imagine new possibilities in our sentencing practices and must conceive of the essential legal and ethical task of the sentencing judge in new terms: an imaginative engagement with the lives of those that they punish.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".