Thank you to our 2019 peer review panel
Bibliographic record
Abstract
The following people have generously taken time out of their demanding work and personal schedules to volunteer as peer reviewers for International Paramedic Practice. For the first time, we are publishing a list of our peer review panel for the year as a small way of offering our sincere grattitude for the extremely important work they do, without which we could not produce high-quality double-blind peer-reviewed content for our readers every quarter. Our peer reviewers are highly valued members of our editorial team. We are grateful for the time, energy, expert knowledge and insight that goes into their constructive comments, which improve the research and writing of our authors, and which help us to publish only those articles that are up to standard and that contribute in some meaningful way to the existing literature.
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 | 0.063 | 0.433 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.069 | 0.151 |
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".