Rethinking Criminal Responsibility for Poor Offenders: Choice, Monstrosity, and the Logic of Practice
Bibliographic record
Abstract
In theory and in discourse, Canadian criminal law insists on the importance of free will, choice, and difference in order to hold someone criminally responsible and to legitimize punishment. Yet legal doctrine is constructed and applied in a very technical and descriptive manner that usually casts aside practical considerations, proceeds on utilitarian grounds, and simplifies what it means to be free, rational, and different. Recent proposals to strengthen or to eliminate the retributive model (e.g., to include in the analysis considerations such as socio-economic disparities and power differential or to definitely shift the discourse toward utilitarian considerations) still rely on assumptions about agency, liberty, and equality that are grounded in contested sociological evidence. As a result, their capacity to promote concrete reform is limited. In this paper, the author draws from the works of Bourdieu and other praxis theorists and argues that their research could shed new light on our understanding of choice and difference—two essential components in the assessment of responsibility. The author concludes by showing what criminal law theory could look like, especially in the case of poor offenders, if reformers were to consider such sociological evidence.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.221 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| 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".