The Floating Signifier of ‘Safety’: Correctional Officer Perspectives on COVID-19 Restrictions, Legitimacy and Prison Order
Bibliographic record
Abstract
The COVID-19 pandemic continues to affect prisons internationally. Existing research focuses on infection data, meaning we do not fully understand how COVID-19 shapes frontline prison dynamics. We draw on qualitative interviews with 21 Canadian federal correctional officers, exploring how the pandemic impacted prison management. Officers suggested inconsistent messaging around COVID-19 protocols reduced institutional and officers' self-legitimacy, fracturing trust relationships with incarcerated people. Furthermore, officers suggest that personal protective equipment such as gowns and face shields took on multiple meanings. We use Lévi-Strauss' floating signifier concept to analyse how individual definitions of 'safety' informed day-to-day prison routines. We conclude by arguing that legitimacy deficits and contested definitions of 'safety' will continue to create uncertainty, impacting prison operations going forward.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.037 | 0.065 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| 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".