Extending Carceral Control Pre-conviction: the Reception, Resistance, and Repercussions of Being Legally Responsible for People Accused of a Crime
Bibliographic record
Abstract
In Canada and the United States, the control, supervision, and rehabilitation of criminalized people often falls on the shoulders of non-state agents and organizations. Surety bail releases are a seemingly clear embodiment of this trend as the courts call upon relatives, friends, and employers to supervise the pre-conviction activity of people accused of a crime. The resulting diffusion of responsibility is said to increase the penal state’s power and control over criminal justice involved individuals while minimizing reputational risks. However, by analyzing data from a year’s worth of bail court observations from a mid-sized jurisdiction in Ontario and interviews with sureties, I find that how friends and family assume the role of surety varies considerably and regularly diverges from court expectations. Because sureties are not legal professionals, their understanding of and ability to enforce court-ordered conditions and report bail violations is primarily shaped not by court instructions and legal mandates but by their ever-changing relationship with the accused, existing biases towards the law, and extenuating life circumstances. In this way, carceral control is not just assumed by sureties but also resisted, ignored, and subsequently transformed in the context of their everyday lives. Under surety bail releases, the governance of accused individuals therefore represents a patchwork of different and sometimes competing modalities that are stitched together by both state representatives and ordinary citizens. Yet, for accuseds and their sureties, this involvement can come at a cost. While surety releases have the potential to improve the accused’s relationship with their friends and family and increase their surety’s willingness to continue their support, the rules of the court are like an omnipresent force that continually threaten to disrupt the ties that bind. Indeed, the constant pressure of having to enforce court-ordered conditions subjects sureties to a more punitive experience, demonstrating punishment drift in action. The results of this study inform a series of recommendations geared towards offsetting the pains of pre-conviction for accused and their loved ones.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".