The Qualitative Historical Origins of Mad Studies in Word and Deed, 1436–1914
Bibliographic record
Abstract
Periodically from the 15th to early 20th centuries, mad people left written accounts of their experiences. Originally written by upper- and middle-class White authors who had both the money and literacy skills to record their thoughts, since the late 19th century, the diversity of mad people’s writings has increased to reflect more representative experiences regarding class, gender and, to a lesser extent, race and disability. The origins of what is now called “Mad Studies” can be found among writers discussed here, though long before such a field came to formally exist. From the 18th century, there were also individuals and organized groups who challenged abusive practices and attitudes toward mad people, whose efforts are precursors to recent activist histories. This article reflects on how Mad Studies is the direct beneficiary of centuries of critical analyses and activism by mad people long before contemporary times.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.041 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".