Bibliographic record
Abstract
Over the past 6 months, we have reviewed every aspect of our editorial process and production, all in an effort to provide you the reader and all prospective authors with the best possible experience that we can.We are excited to debut this new look issue with an Indigenous health theme and are grateful for the input of Guest Editor Professor Shane Houston for his valuable insights in this important field.Your journal is now divided into three distinct sectionsblue, red and green.The blue section starts us off and contains important news, perspectives, debates, lessons from practice, clinical snapshots with an image, a new educational series, information on medical law and ethics, historical vignettes, and other articles that are of broad interest to all.For example, in this issue we are proud to launch our new clinical skills series that we hope will be of value not only to doctors in training and medical students but also to experienced clinicians.This year we will also commence a new series in innovations in medical education and a series to demystify research methodology and statistics.
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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.075 | 0.046 |
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".