Bibliographic record
Abstract
I am impressed by the review by Brian Cooper of psychiatry in Britain.1 He is an erstwhile colleague of mine. As a Professor of Psychiatric Epidemiology he gave it forensic attention. I have been in psychiatry since 1964, and have been an academic in London, Hobart, Toronto, and St Louis. Latterly, I have been a clinician in Plymouth. I have to say that my English experience in recent years has been disappointing and unexciting. Professor Cooper talks of the decline of psychiatry as a medical career and he is absolutely right. As said, it is not recruiting sufficient British graduates. Furthermore, the situation is becoming unacceptable as the subject is thought of by some as an odd specialty. However, before all is lost we need to remember certain epidemiological facts. One in five people in the UK and other countries will develop an affective disorder (anxiety and depression) during their lifetimes; and the World Health Organization has predicted that by 2020 depressive illness will be the commonest medical condition in the world. So let us get serious.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".