Partners in health, partners in crime : exploring the boundaries of criminology and sociology of health and illness
Bibliographic record
Abstract
Notes on Contributors. 1. Introduction: Connecting Criminology and Sociology of Health and Illness: Stefan Timmermans and Jonathan Gabe. 2. Towards Generous Constraint: Freedom and Coercion in a French Addiction Treatment: Emilie Gomart. 3. The Cause of Death Vs. the Gift of Life: Boundary Maintenance and the Politics of Expertise in Death Investigation: Stefan Timmermans. 4. Violence Against Doctors: A Medical(ised) Problem? The Case of National Health Service General Pracitioners: Mary Ann Elston, Jonathan Gabe, David Denney, Raymond Lee and Maria Oa Beirne. 5. The Emergence and Implications of a Mental Health Ethos in Juvenile Justice: Sarah Armstrong. 6. Contesting the Text: Canadian Media Depictions of the Conflation of Mental Illness and Criminality: Riley Olstead. 7. Actor Networks, Policy Networks and Personality Disorder: Nick Manning. 8. Temporarily Insane: Pathologising Cultural Difference in American Criminal Courts: Sita Reddy. Index.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".