MétaCan
Menu
Back to cohort
Record W2346415445 · doi:10.1017/cbo9781139507554.015

Postwar Sterilization

2013· book-chapter· en· W2346415445 on OpenAlexaff
Randall Hansen, Desmond King

Bibliographic record

VenueCambridge University Press eBooks · 2013
Typebook-chapter
Languageen
FieldImmunology and Microbiology
TopicMedical Device Sterilization and Disinfection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSterilization (economics)MedicineBusiness

Abstract

fetched live from OpenAlex

Mental health institutions – geographically separated from the rest of society, hierarchically ordered, and governed by a clear system of rewards and punishments – were established in an aura of optimism about their capacity for improving mental health. Indeed, the basic tenets of the moral treatment philosophy from which they took inspiration were antithetical to cruelty and abuse. Their grand architecture, attractive grounds, and professional ethos seemed to make them the very opposite of the torture chambers of the past. We have no reason to doubt that their superintendents were, overall, scrupulous individuals committed to their work. Yet, within the space of a few decades, these institutions became sites of intimidation, fear, abuse, and even torture. Abuse within institutions for the mentally ill and/or cognitively challenged is nothing new. Some of the earliest reports from inside institutions attest to it. In 1906, a drifter named John W. McCarthy on the American West Coast had run out of money. He related his dire straits to an acquaintance at his boarding house who was a former attendant at the Southern California State Hospital at Patton. The man told him that the hospital was always looking for workers, and McCarthy secured on this advice a position. Not long after, he visited a journalist and friend, Arthur L. Dunn, in Los Angeles, and reported that the institution was a hellhole of abuse. Dunn, sensing an angle, spoke to his editor at the Los Angeles Record , who told him to investigate the story. Dunn traveled to Patton and applied for a job at the hospital. Dunn had no references and no experience. When asked why he wanted the position, he said that he had “learned of the place from a cigar man at Colton.” He got the job.

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)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.017
GPT teacher head0.193
Teacher spread0.176 · 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
GenreOther

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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueCambridge University Press eBooksSame topicMedical Device Sterilization and DisinfectionFrench-language works237,207