Carceral Tours and the Need for Reflexivity: A Response to<scp>W</scp>ilson,<scp>S</scp>pina and<scp>C</scp>anaan
Bibliographic record
Abstract
Abstract In previous work (seePiché andWalby ) we argued that carceral tours as commonly practised have limited pedagogical and research value, and contribute to the degradation that prisoners experience. Wilson,Spina andCanaan ( ) have recently criticised our position on carceral tours. In this rejoinder, we critique the methodological approach and the logic ofWilson and colleagues. We argue thatWilson and colleagues fail to consider how organisational policies and power relations shape carceral encounters. In this way, we reiterate our call for greater reflexivity and critical scrutiny regarding carceral tours.
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 | 0.078 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.128 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.035 | 0.051 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".