Bibliographic record
Abstract
The cover of this issue doesn't give away the exciting change contained within the pages. If you haven't already looked at the masthead, please flip back a few pages. At the top of the masthead you will find the name of the new editor of the Oncology Nursing Forum (ONF): Anne Katz, RN, PhD. Anne assumed editorial leadership of this flagship journal effective with this issue, and we are honored to have her at the helm. She is a clinical nurse specialist at the Manitoba Prostate Centre, an adjunct professor in the School of Nursing at the University of Manitoba, and a sexuality counselor for the Department of Psychosocial Oncology, CancerCare, all in Winnipeg, Canada. She has served as the editor for the journal Nursing for Women's Health, the clinical practice journal of the Association of Women's Health, Obstetric, and Neonatal Nursing, and also is a contributing editor for the American Journal of Nursing.
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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".