“He’s in jail now and I don’t feel bad”: Analyzing Sureties’ Decisions to Report Bail Violations
Bibliographic record
Abstract
The control, supervision, and rehabilitation of criminalized people often falls on the shoulders of non-state agents and organizations. Surety bail releases are a 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. According to the law, sureties must report all bail violations to the police; 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. Yet how sureties carry out this role in the community remains unexplored. Using data from 36 interviews with sureties in Ontario, Canada, we find that how friends and family assume the role of surety varies considerably and regularly diverges from court expectations. Despite the general commitment sureties show towards supervising the accused, decisions to report an accused vary based on the perceived severity of the act, the fairness of the conditions, the accused’s best interests, and their own bias towards the law. In this way, responsibility for carceral control is not just assumed by sureties but also resisted, ignored, and subsequently transformed in the context of their everyday lives. Understanding how the decision-making process of sureties works in practice is important for informing recommendations geared towards offsetting the pains of pre-conviction for the accused and their loved ones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".