Madness in the Archives: Anonymity, Ethics, and Mental Health History Research
Bibliographic record
Abstract
Historians have long been vexed by the challenges of using patient records as primary sources. Lurking behind the many methodological and interpretative challenges are ethical questions involving the status and identity of the dead patient. What rights do the deceased maintain over their medical records? What ethical obligations do researchers have in analyzing these historical records and, in particular, to preserving the anonymity of patients? Do professional duties diminish the further back one goes in time? Do patients suffering from mental distress differ from other “medical” patients in the ethical regard owed to them? Now that we know about the care of the mentally ill outside of formal institutions during the era of the asylum, is there something intrinsically different about the status of individuals once they entered formal institutions? Or do the designations of “lunacy” or “idiocy” on extramural death certificates or in census enumerators’ schedules oblige a similar professional discretion? Is the concern over confidentiality giving way to a new emphasis on returning names (and agency) to vulnerable groups in the past? This paper explores these questions, ones that lie at the heart of what we do as historians of disability, medicine, and society.
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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 | 0.063 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.049 | 0.211 |
| Scholarly communication | 0.028 | 0.024 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".