Picking battles: Correctional officers, rules, and discretion in prison
Bibliographic record
Abstract
Abstract To outsiders, prisons vacillate between visions of regimented order and anarchic disorder. The place of rules in prison sits at the fulcrum between these two visions of regulation. Based on 131 qualitative interviews with correctional officers across four different prisons in western Canada, we examine how correctional officers understand and exercise discretion in prison. Our findings highlight how an officer's habitus shapes individual instances of discretionary decision‐making. We show how officers modify how they exercise discretion in light of their views on how incarcerated people, fellow officers, and supervisors will interpret their decisions. Although existing research often sees a correlation between “rule‐following” by incarcerated individuals and official statistics on such misdeeds, our data highlight that official statistics on rule violations do not easily represent the rate or frequency of such misbehavior. Instead, these numbers are highly discretionary organizational accomplishments. Our findings advance an appreciation for correctional officer discretion by focusing on the range of factors officers might contemplate in forward‐looking decisions about applying a rule and how they rationalize the nonenforcement of rules.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".