Thirty Years of Consulting to Child Welfare
Bibliographic record
Abstract
In 1962 b.c.—that is, before Caplan-I finished my residency in child psychiatry and began my first job, as psychiatrist in charge of two inpatient wards of what was then the only children’s psychiatric hospital providing long-term in-patient care for severely disturbed children and adolescents in the Province of Ontario. During my training, I had never been exposed to residential treatment, and I approached my new job wondering whether my newfound charges had much in common with the constricted and primarily neurotic children that I had treated during my residency. Nor was I convinced that the play therapy and family therapy that were the major weapons in my therapeutic arsenal during my training would have much relevance to this radically different group of patients who, at times, seemed to have more in common with wild animals than with the only other child psychiatric patients I had ever known. Right from the start I was very much aware that something very different was expected of me. But what?
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.021 |
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".