Getting the usual treatment: research censorship and the dangerous offender
Bibliographic record
Abstract
In the course of finishing dissertation research, this author encountered a wall of opposition from the Canadian penitentiary service and parole board to his proposal. For political reasons they opposed research on dangerous offenders from the perspective of ‘convict criminology’, concluding: ‘This proposal does not reflect CSC [Correctional Service of Canada] priorities and service objectives, and would result in disruption to institutional operations.’ For a period of two months, this criminologist was barred from all penitentiaries in Ontario and could not interview any prisoner. Complaints were made to Members of Parliament including the then‐Solicitor General of Canada, as well as the Office of the Correctional Investigator; even the University tried to censor the project. This article seeks to place this episode in the context of the historic marginalization to which critical and convict criminology have been subjected. It will document how the state controls the criminological research agenda and what happens when ‘voices from below’ want to have a say in penological research. Of related interest will be a discussion of how a university research ethics committee, in conjunction with the penitentiary service, tried to stop this project.
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 | 0.038 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".