Bibliographic record
Abstract
T he Canadian Psychiatric Association (CPA) has announced that a new Editor-in-Chief will be appointed for fall 2014, when my mandate ends.I am grateful to the CPA for this exciting job.In recent years, The Canadian Journal of Psychiatry (The CJP) has focused on original research and systematic review papers in all areas of psychiatry.The In Review series, which I helped to develop in 1997, is the most frequently referenced.The topics are chosen by the Editorial Board, and Guest Editors invite experts to review the literature.Readers of this section will note how much psychiatry has progressed, but also how much it still has to learn-research methods are only beginning to illuminate clinical issues, and many treatment methods have not yet been shown to be effective.In 2013, the January issue will focus on Psychosis in 3 In Reviews (specifically, risk of psychosis, prodromal symptoms, and social causes) and the February issue will have 2 In Reviews on Gene-Environment Interactions (relating to major depressive disorder and posttraumatic stess disorder) and Neuroplasticity (that is, schizophrenia as a neuroplasticity disorder).Future issues will deal with Disaster Mental Health Response, Melancholia, Behavioural Additions, Cognitive Remediation, Depression Psychotherapy, and more.
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 | 0.181 | 0.508 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.068 | 0.057 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.056 | 0.103 |
| Open science | 0.013 | 0.022 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.086 | 0.038 |
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".