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Record W2616700487 · doi:10.1163/18253911-03202002

Between the Mind Twist and the Brain Spot

2017· article· en· W2616700487 on OpenAlexaff
Tara H. Abraham

Bibliographic record

VenueNuncius · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNeuropathologyGermanPsychopathologyPsychologyPsychoanalysisBrain diseaseMedicinePsychiatryPathologyDiseasePhilosophy

Abstract

fetched live from OpenAlex

This paper explores the material and visual practices that defined studies of psychopathology in early twentieth-century American medicine, through a close look at the work of neuropathologist Elmer E. Southard (1876–1920). As a discipline sitting at the intersection between laboratory and clinical practice, neuropathology has received little attention from historians of the brain sciences. Unlike the neurologist, who was interested in treating patients and saving lives, the neuropathologist often encountered patients following death, and studied the brain for signs of pathology during autopsy. Trained in a German tradition of laboratory pathology, Southard has been cast as a somaticist with respect to psychopathology. By examining Southard’s medical and philosophical writings, I present a more nuanced analysis of the role of brain pathology in Southard’s vision of disease etiology, his views on the foundations of psychiatry, as well as a more vivid picture of the sorts of material practices that defined the work of the neuropathologist.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.048
Scholarly communication0.0060.008
Open science0.0000.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.067
GPT teacher head0.298
Teacher spread0.231 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2017
Admission routes1
Has abstractyes

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