Bibliographic record
Abstract
taking over the Helm Tackling this first editorial has led me to reflect on the leadership of my predecessor and mentor, Dr. Dorothy Pringle.Having worked closely with her for several years now, I have had the privilege and benefit of watching her leadership in action.Under her watch, this journal has emerged as a high-quality publication with a broad distribution and significant readership of both the print and online versions.Because of the quality of the papers published and the breadth of dissemination, authors nationally and internationally are now competing for the opportunity to publish in CJNL.Dr. Pringle is one of those individuals who have profoundly influenced my career over the past decade.She has my utmost respect and high regard for her career contributions to nursing practice, research, education and leadership in this country.I am the first to acknowledge that to walk in the steps of a giant is humbling and daunting.But it is my sincere hope that through her tutelage I have garnered the requisite insights to not only sustain but further advance the calibre and profile of the journal on the international stage.
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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.129 | 0.173 |
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".