On the history of cultural psychiatry: Georges Devereux, Henri Ellenberger, and the psychological treatment of Native Americans in the 1950s
Bibliographic record
Abstract
Henri Ellenberger (1905-1993) wrote the first French-language synthesis of transcultural psychiatry ("Ethno-psychiatrie") for the French Encyclopédie Médico-Chirurgicale in 1965. His work casts new light on the early development of transcultural psychiatry in relation to scientific communities and networks, particularly on the role of Georges Devereux (1908-1985). The Ellenberger archives offer the possibility of comparing published texts with archival ones to create a more nuanced account of the history of transcultural psychiatry, and notably of the psychological treatment of Native Americans. This paper examines some key moments in the intellectual trajectories of Devereux and Ellenberger, including Devereux's dispute with Ackerknecht, the careers of Devereux and Ellenberger as therapists at the Menninger Foundation (Topeka, Kansas) in the 1950s, and their respective positions in the research network developed by McGill University (Montreal, Quebec) with the newsletter Transcultural Research in Mental Health Problems Finally, I consider their ties to other important figures in this field as it transitioned from colonial medicine to academic medicine, including Roger Bastide (France), Henri Collomb and the Ortigues (France and Africa), as well as Eric Wittkower and Brian Murphy (Canada) and Alexander Leighton (United States and Canada).
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.050 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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".