Bibliographic record
Abstract
I am delighted to announce the appointment of Ted Schrecker and Eugene Milne as the new editors of the Journal of Public Health. Ted is Professor of Global Health Policy at the School of Medicine, Pharmacy and Health at Durham University. He moved to the UK from Canada in 2013 and has extensive editorial and manuscript review experience. Eugene is Director of Public Health in Newcastle and also an honorary professor at Durham University. Ted and Eugene have been working alongside Gabriel Leung since January; Gabriel will formally step down as joint editor at the end of June. On behalf of the Faculty of Public Health, I would like to record my sincere thanks to both Gabriel and Selena Gray, who stood down as editor in 2013, for all the time, energy and commitment they gave to the journal over the past seven years. The journal has enjoyed considerable success under their leadership, which has resulted in a significant increase in its impact factor. We very much look forward to working with Ted and Eugene on its further development.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.106 | 0.077 |
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".