Bibliographic record
Abstract
In a chapter for Mad Matters – A Critical Reader in Canadian Mad Studies ( LeFrançois et al, 2013 ), the author offered a “recipe” for developing Mad Studies in the academy. This chapter picks up where that one left off. In November 2018, Kathy Boxall and the author met via Skype for a conversation about taking Mad Studies back out into the community. Because Mad Studies is founded in the stories of Mad people, it’s wrong to sequester those stories. Mad people should know their own history of resistance and struggle. One way to make that happen is to hold workshops for survivor groups; another is to create research teams that include members of the Mad community. The author was part of a group that endowed a bursary in Mad People’s History; the bursary pays the fees of a member of the Mad community.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.037 | 0.025 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".