Anticipating prison face work: Dramaturgical risks anticipated by correctional officer recruits
Bibliographic record
Abstract
Abstract There is increasing recognition that correctional officers (COs) serve a crucial role in their work in relation to communications underpinning discretion, especially regarding interactions with prisoners. This article examines attitudes and perceptions among Canadian federal correctional officer recruits (CORs) regarding what they anticipate are the greatest challenges they will face as new COs. We examine these discussions through the framework of Goffman's dramaturgical model of face work, especially face work within the ‘total institution’ of prisons. Our findings centre on anticipated challenges of building rapport with prisoners, including the requirements to monitor one's demeanour and ‘face work’. Characterisations of prisoners as inherently manipulative factor into CO anticipations of interactional challenges. We also consider the role that ‘soft power’ has in facilitating CO‐prisoner rapport and trust, which, we argue, ultimately undergirds opportunities in prisons to facilitate prisoner social change and successful community reintegration and desistance from crime.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".