Labor, Depletion, and Disconnection: Leisure as Restoration for Uniformed Public Safety Personnel
Bibliographic record
Abstract
In Canada, uniformed public safety personnel (PSPs) are systemically uniformed and placed into positions where they are exposed to both primary and/or secondary/vicarious trauma while “on duty.” PSPs, particularly law enforcement officers (police) also navigate historical and cultural traumas and oppressions that are symbolized by the materiality of the uniform. Menakem’s work delves into the profound impact of intergenerational trauma that lives in white, Black, and (uniformed) police bodies, and how healing must begin on an individual level before it can extend to a collective healing. Drawing on research from a study conducted with PSPs, this paper helps to address the tensions surrounding labour, trauma, and wellness faced by individuals systematically uniformed to perform roles in public safety and care between government and community. Specifically, we highlight three narratives themes, (1) “It is not if, it is when”—The Inevitable Nature of Trauma Labour, (2) “You should be able to handle anything”—Internalizing Oppressive Mental and Emotional Narratives, and (3) “Deal with your shit on your own time”—Leisure as a Practice of Care, a Space of Healing, and Site for Restoration. We discuss leisure as restoration to promote healing for uniformed PSPs who endure trauma as part of their labours (individual healing), as a means of creating a rippling effect that fosters collective healing.
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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 | 0.003 | 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 | 0.030 | 0.032 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| 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".