Discretion as weakness: Exploring the relationship between correctional officers' attitudes toward discretion and attempted boundary violations
Bibliographic record
Abstract
Research paints discretion as a tool correctional officers (COs) use to navigate their work. Discretion helps COs gain compliance and resolve conflicts amicably, and officers sometimes use it to improve relationships with incarcerated people. However, research also suggest that COs' reliance on discretionary power may produce harmful complications, undermining institutional regulations and creating conditions for serious rule violations. Little quantitative analysis exists on how CO discretion impacts prison operations, making the broader impact of discretion unclear. To address this gap, we use open-access data collected between 2017 and 2018 ( Griffin & Hepburn, 2020 ). We then test whether a CO's attitude toward discretion may correspond with attempts from incarcerated people to encourage boundary violations. Results show that COs with more liberal attitudes toward discretion correspond with higher odds of being approached by incarcerated people to violate boundaries. Black COs have lower odds of being approached for minor boundary violations, while women officers have higher odds of having incarcerated people try to initiate an inappropriate relationship. Findings show that liberal attitudes among COs toward discretion may encourage incarcerated people to violate the most consequential prison rules. We conclude by discussing the implications for future research.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".