Good Citizens with the Wrong Arguments: A critical comment on Sullivan's Social Diversion
Bibliographic record
Abstract
Abstract In the wake of the Ontario Court of Appeal's decision in R v Sullivan, striking down legislative limits on the extreme intoxication defence in Canada, significant public debate arose about how the intoxication defence applies to sexual assault. Currently, the social debate has focused on implications for individual criminals and victims. This paper will argue the terms of the debate have dodged the larger social issues at the intersection of mental health, addiction, gender, and racial inequity, and poverty. These issues have been sidelined in the social discourse due to the efforts of institutions to divert away from the role they play in the underpinnings of crime and victimization. To affect social diversion, institutions notably leverage harm decisionism rhetoric to uphold existing responsibilization paradigms. Thus, to meaningfully affect social change, the call to action is rather than arguing amongst themselves within the current ideologically driven parameters, activists should deconstruct the discourse to hold institutions accountable for the inequitable social structures they uphold. Analysis of the Sullivan case grounded in social statistics and penal philosophy demonstrates how institutions can covertly pit activists against one another and uphold a socially misinformed status quo. Rather than attack criminals or victims affected by their social circumstance, activists should expose this process of how crime occurs and persists to victimize: institutionally supported responsibilization of disadvantaged groups due to an unwillingness to allocate appropriate resources to address their needs.
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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 | 0.037 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.054 | 0.070 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.076 | 0.083 |
| Insufficient payload (model declined to judge) | 0.003 | 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".