Psychiatry and Fads: Why is This Field Different from All other Fields?
Bibliographic record
Abstract
Fads in psychiatry are little more than bad ideas with short half-lives. They have arisen because of the great discontinuities that have swept psychiatry unlike other specialties in the 20th century: the transition in the 1920s from asylum-based biological psychiatry to psychoanalysis, and the transition in the 1960s from psychoanalysis to a biological model based on psychopharmacology. In no other medical specialty has the knowledge base been scrapped and rebuilt, and then again scrapped and rebuilt. In these great transitions, when psychiatry each time has had to reconstruct from scratch, bad ideas have crept in with good. Psychiatry, in its heavy use of consensus conferences, is often unable to employ science as a means of discarding fads, which, once installed, are often difficult to remove. Each of the great paradigms of psychiatry in the last hundred years has given rise to fads, and psychopharmacology is no exception, with faddish uses of neurotransmitter doctrine claiming centre stage. Only when psychiatry becomes firmly linked to the neurosciences will its subjugation to the turbulence of faddism be moderated.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".