Bibliographic record
Abstract
Article Abstract Because this piece does not have an abstract, we have provided for your benefit the first 3 sentences of the full text. This textbook has 19 chapters by 40 expert authors, addressing (1) course and outcome, (2) pathobiology, (3) juvenile psychosis, (4) early psychosis, (5) pharmacologic treatment, (6) chronicity and treatment resistance, (7) cognitive-behavioral treatment, (8) rehabilitation, (9) community treatment, (10) treatment nonadherence, (11) suicide, (12) violence, (13) substance abuse, (14) medical comorbidity, (15) interactions with patients and their families, (16) male-female differences, (17) genetics, (18) economics, and (19) personal reflections by 5 persons diagnosed with schizophrenia. Chapters average 22 ± 11 pages in length (72% as text, 28% as references); the longest is on pharmacotherapy (61 pages). A majority (68.4%) of chapters have coauthors from different countries: United States (55.0%), England (22.5%), Australia (10.0%), other European countries (7.5%), Canada (2.5%), and Japan (2.5%), although most of the material represents a largely British Commonwealth-American perspective. As with most multiauthored texts, there is some variation in approaches among chapters, but only minor overlap of material.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".