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Record W2413180315 · doi:10.1163/9789004333598_007

Delusions of Gender?: Lay Identification and Clinical Diagnosis of Insanity in Victorian England

2003· article· en· W2413180315 on OpenAlexaff
David Wright

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicHistorical Psychiatry and Medical Practices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInsanityLunaticPsychiatric diagnosisPsychiatryIdentification (biology)MedicineRepresentation (politics)EpidemiologyMedical diagnosisPsychologyCriminologyPolitical scienceLawSchizophrenia (object-oriented programming)Pathology

Abstract

fetched live from OpenAlex

This chapter examines the lay identification and medical diagnosis of patients admitted to public mental hospitals in Victorian England through an analysis of over 1,500 admissions to the Buckinghamshire Lunatic Asylum. It demonstrates three things: women were institutionalised in numbers commensurate with their representation in the adult population; the certification of the insane was not dominated by male informants; and there is no empirical evidence to suggest that gender played a dominant role in the decision over the selection of particular psychiatric diagnoses. This chapter suggests future areas of research for a new historical epidemiology of mental symptoms.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.380

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.078
GPT teacher head0.410
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2003
Admission routes1
Has abstractyes

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