What it takes to be a “Good” correctional officer: Occupational fitness and co-worker expectations from the perspective of correctional officer recruits in Canada
Bibliographic record
Abstract
Selecting individuals who are the right "fit" for correctional work is not an easy task for prison administrators because of the dangerous nature of correctional work and the centrality of prison employees in the prisoner's rehabilitation process. We analyze fitness for correctional work from the employee's perspective, complementing the scholarship focused on the employer's view. We measure occupational fitness in terms of co-worker expectations, analyzing 104 semi-structured interviews conducted with Federal Canadian Correctional Officer recruits in 2018/2019. Recruits in our sample expected a correctional officer to be accountable, reliable, and confident. Understanding the mind-set of new hires provides insights into the correctional officer role and allows employers to align employer-employee expectations, as well as review training and recruitment, which can improve the employee well-being and reduce turnover rates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".