Bibliographic record
Abstract
Happy New Year to all and I hope you are looking forward to another exciting year for the International Journal of Mental Health Nursing as I am. I am very excited to introduce the new structure and ofcourse the new members of the Editorial Board for the IJMHN. The call for Expressions of Interest was met with a large number of enquiries and applications. The fact that so many of these were from outside Australia demonstrates the place the journal new holds within the international community of mental health nurses, while still retaining a strong interest within Australia. It was the source of both pleasure and anguish that the number of EOIs received far outnumbered the positions available. The pleasure reflects the popularity of the IJMHN within professional and scholarly communities and the willingness of the journal’s readership to become actively involved in the future of the journal. The anguish comes from not being able to convert all of that interest into positions on the Board. To those applicants who were not already reviewers, I have extended an invitation to contribute to the journal in this important capacity and many have already accepted. The ongoing success of the IJMHN depends in no small part on the availability and willingness of reviewers to provide valuable feedback as part of the peer review process.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.540 | 0.547 |
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".