Bibliographic record
Abstract
The journal is now in its eighth year of publication and it is reaching a truly international audience of researchers, educators and practitioners. The countries involved stretch across the world, from Scandinavia in the north, to Australasia in the south, China, Thailand and Japan in the east and the USA and Canada in the west. Subscriptions are received from 31 different countries. During those years of publication many papers by overseas contributors have been published, covering that same geographical spread and involving 12 different countries and many different cultures. Our Editorial Board reflects this range as far as possible and we also have the special support of Receiving Editors for North America and Australasia, where many of our overseas subscribers and contributors are based. In order to demonstrate further the international links enjoyed by the journal, a series of Guest Editorials is planned to introduce issues and concerns regarding mental health care and nursing in the various countries. It is hoped that these will encourage links between practitioners in the different countries and also, perhaps contributions to our Commentary Section where issues raised in the Guest Editorials may be aired further. The first Guest Editorial will be published in the October issue and will be by our Receiving Editor from Australia, Professor Colin Holmes. Guest Editorials will then follow in subsequent issues in no particular order and implying no hierarchy or priority. I hope that these Editorials will become eagerly awaited sections of the journal.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.040 |
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".