Correctional Officers and Social Media: Policies, Challenges, and Vulnerabilities
Bibliographic record
Abstract
In this article, we draw from 159 qualitative interviews with Canadian correctional officers (COs) exploring their experiences and attitudes regarding social media. We frame our study within the “synoptic” mode of surveillance and public visibility—referring to the many observing the few—impacting public safety personnel, exploring the perceived vulnerabilities of COs, including toward prisoners, management, the organization, and the public. We highlight recent research on COs, especially in the Canadian context, and review the synoptic surveillance implications of social media in society. Our findings highlight four interrelated areas: COs (sometimes lack of) awareness of Correctional Service Canada (CSC) policies regarding social media use; challenges they experience online; the central role of privacy; and strategies they use to manage challenges and maintain privacy, especially considering their role as public representatives. We conclude by discussing how the use of social media produces new vulnerabilities for COs in public spaces and suggest future directions for research and practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.016 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".