Bibliographic record
Abstract
Earlier this year I published a book entitled, Mad Studies: The Basics. This book introduces Mad Studies as a critical orientation akin to, and often intersecting with, crip theory, queer theory, critical race studies, feminism, decolonialism, and other critical studies. As this suggests, Mad Studies is not simply the study of mental health and illness. Rather, Mad Studies embraces “a liberationist desire to resist, transform, and abolish oppressive practices within the systems that create marginalization, and implement frameworks and responses to madness and distress that are grounded within the collective knowledge of those deemed Mad” (pp. 1-2). I wrote this book as a contribution to Mad Studies field-building; despite its growth, there are still many who have never heard of Mad Studies. The field is sometimes still referred to as ‘emerging,’ but with a robust body of literature, an international journal, and many post-secondary course offerings, it’s safe to say that Mad Studies is an established field in its own right. The book showcases some of the many contributions of Mad Studies and demonstrates that it is no longer emerging; it has arrived!
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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.045 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".