The Bad, The Ugly, and The Horrible: What I Learned About Humanity by Doing Prison Research
Bibliographic record
Abstract
Every Canadian academic conducting research with humans must submit an ethics application with their university’s Research Ethics Board. One of the key questions in that application inquired into the level of vulnerability of the interviewees. Filling in that question, I had to check nearly every box: the interviewees were incarcerated, old, under-educated, poor, Indigenous or other racial minorities, and likely had mental and physical disabilities. However, it was not until I met John that I understood what all those boxes actually meant. They were signalling that I was entering a universe of extreme marginalization—the universe of the forgotten. I learned then what we, as a society, look like at our worst, when no one watches, when there is no money to be made and no votes to be gained. Entering this universe has allowed me to identify some broader socio-legal issues, applicable across prison demographics, from gaps in prison health care and punitive carceral responses to health needs, to substantive and procedural access to justice for violations of rights in prisons and the role of health care and access to justice in achieving the rehabilitative and reintegration goals of sentencing.
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 | 0.069 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.073 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".