Bibliographic record
Abstract
Why bother with peer review in a world where quick access to information is of paramount importance? Does peer review just delay the flow of information to the readership, or is it a way to restrict information and thus to create conformity in the medical literature (in other words, a form of censorship)? As far as I am concerned, peer review is all about quality control and the integrity of published information. But do you, as a reader of CJHP, know what the peer review process actually involves? What’s in it for the reviewers, the authors, and the readers? At CJHP, the peer review process starts with a preliminary review of each article by one of the associate editors to establish if the manuscript is of interest to our readership. After this initial assessment, usually 2 reviewers with pertinent practice experience are selected from the journal’s pool of volunteer reviewers. The reviewers are given 4 weeks to go over the manuscript, from the perspective of both scientific content and presentation, and to provide constructive feedback. The comments of the assigned associate editor and the reviewers’ evaluations are then sent back to the authors. The authors are asked to address the reviewers’ comments before the paper is again considered for publication. To minimize bias, the identity of the reviewers and the authors is not divulged (double blinding). Once the authors have responded to the reviewers’ comments, the editor reviews the document again to ensure that all of the comments have been addressed. The last step before publication is copy editing, where the focus is on style, format, and grammar. As you may have realized, the whole process is lengthy and intensive (unpublished report from CJHP strategic planning workshop, January 2006).
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 | 0.445 | 0.752 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.014 | 0.054 |
| Scholarly communication | 0.097 | 0.054 |
| Open science | 0.012 | 0.028 |
| Research integrity | 0.030 | 0.038 |
| Insufficient payload (model declined to judge) | 0.020 | 0.023 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".