‘Dirt, Death and Danger? I Don't Recall Any Adverse Reaction …’: Masculinity and the Taint Management of Hospital Private Security Work
Bibliographic record
Abstract
Drawing on an ethnographic narrative written by one of the authors following his resignation from a hospital private security team in O ttawa, Canada and interview data gleaned from eight security men (all former colleagues), this article explores how hospital private security officers draw on discourses of masculinity to navigate the ‘dirty’ boundaries of their work, and to preserve their alpha‐guard statuses as controlled, autonomous and authoritative subjects. We found that hospital guards manage and deflect taint status by emphasizing their resiliency, emotional detachment and enthusiasm towards morbid, disturbing and dangerous tasks. Guards who seek to challenge these components of the job may be subject to gender harassment and reprisal from other guards, senior security officials and nursing staff. Overall, these narratives call attention to the necessity of hospital training programmes, de‐briefing exercises and best‐communication practices that promote the physical and emotional well‐being of persons who engage in intensive forms of dirty work.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".