Research Access Barriers as Reputational Risk Management: A Case Study of Censorship in Corrections
Bibliographic record
Abstract
Many criminal justice organizations, including the Correctional Service of Canada (CSC), grant research access through their own research branches. I attempted to interview CSC employees for research about programming and policy in relation to in-prison substance abuse, but access was denied. I have turned my experience into a case study, where I treat my correspondence with CSC as a unique source of data. Although access-denied case studies have appeared in the literature on conducting prison research, I apply a novel lens, reputational risk management, to expand the conceptual toolkit for future researchers. I also use interview data from a sample of 16 participants – former CSC senior administrative officials, former CSC front line staff, and external stakeholders – to supplement my analysis. The case study and interviews reveal new insights regarding access barriers, censorship, and the insular character of CSC research. These restrictions can lead to adverse consequences such as the (re)production of limited knowledge about corrections and the curtailment of innovative solutions to problems. I thus encourage researchers to further refine the application of reputational risk to criminal justice settings and to be persistent in their efforts to access correctional organizations.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.039 | 0.018 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| 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".