Bibliographic record
Abstract
The experiences of crip and mad people—as well as the disciplinary homes of crip theory and mad studies—have rarely been brought together in any synthesised manner. In this article, I bring crip theory and mad studies together to explore the similarities, intersections, and points of departure. The article starts by exploring the similar life experiences between crip and mad bodies, including: familial isolation; shame, guilt, and essentialism; stereotypes and discrimination; experiences and rates of violence; the power of diagnostic labels; and, passing and ‘coming out’. The discussion then moves to explore the theoretical overlaps between crip theory and mad studies, including: (strategic) essentialism vs constructionism; opposition to norms; subversion and transgression as political tools; and, the problematisation of binaries. The article then meditates on the question of combining these two schools of thought to help forge a collective politics, and speculates about the political methodologies of cripping and maddening dialogues.
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 | 0.033 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.016 | 0.164 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".