The Emotional Roller Coaster of Correctional Officers: Not Just a Job
Bibliographic record
Abstract
Emotions are everywhere in our criminal justice system. However, society does not yet have a consensus on when to incorporate emotions and when to exclude them at the different stages and processes of the criminal justice system. This paper examines literature based on the role of emotions in punishment, specifically, how correctional officers are affected by emotionally intense work environments. In order to mobilize justice, society relies on correctional institutions to control the conditions of custody and punitive consequences. Prisons are emotionally painful not only for inmates, but officers as well. Emotions such as anxiety, sadness, or disgust can impact the way that officers control their own emotions and how they treat offenders. Punitive conditions and long-term exposure to violent and emotionally disturbed inmates becomes difficult for correctional officers to separate their work-selves from their societal-selves. The lack of training or preparation by Correctional Services Canada contributes to officers’ deteriorating mental health. Further, gender differences between female and male correctional officers are not recognized in the minimal training that is offered. The ‘one size fits all’ approach continues to be employed when training officers on how to display and control emotions in punitive settings. The emotional intelligence training and practices that Correctional Services Canada offers is inadequate in order to appropriately prepare front line-workers with the emotional longevity to govern inmates. The existing issues that are reported on job posting websites and government sources continue to support the scholarly literature on emotional hardships that correctional officers experience.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".