Reflections on Asylum Archives and the Experience of Mental Illness in Paris
Bibliographic record
Abstract
This article is a personal reflection on the challenges and rewards of doing research on the social history of mental illness and health. The author uses her experiences with the archives of a Parisian psychiatric hospital to discuss some ways of dealing with an overwhelming mass of archival material and the inevitable frustrations and silences that result from trying to do history from the patient’s point of view. The importance of such archival research on mental illness is discussed within the context of a long history of French efforts to provide health care for “citizen-patients.” The article argues that such archives not only provide a wealth of material on the history of illness but that they offer important perspectives on other political and social issues, including the development of the welfare state, the maintenance of public order, and the varied experiences of citizenship.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.052 | 0.059 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".