An examination of whether work culture influences victimization and harassment of federal correctional officers
Bibliographic record
Abstract
The purpose of this paper was to examine possible factors that may contribute to workplace harassment for correctional officers employed with the Correctional Service of Canada by examining the results from the 2019 Public Service Employee Survey (PSES). An analysis of the results indicates that 36% of correctional officers reported harassment on the job. The most common types of harassment that correctional officers reported were offensive remarks, unfair treatment, being excluded or ignored, aggressive behaviour, personal attacks, and humiliation. With respect to the source of harassment, the findings indicate that supervisors and managers were the leading source of those engaging in harassment behaviours within CSC. Fear of reprisal was the most common reason reported for why correctional officers did not file a complaint of harassment. The most common operational and organizational stressors reported were not enough employees to do the work, pay or other compensation-related issues, lack of control or input in decision-making, competing or constantly changing priorities, lack of recognition, and lack of clear expectations. An analysis of CSC’s response to harassment revealed that current initiatives are ineffective and may perpetuate workplace harassment. Several recommendations are made on how CSC should address harassment going forward, such as utilizing human resources and establishing a complaint process that is free of conflict of interest and employs an external independent review body to oversee the process.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".