Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
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".