Bibliographic record
Abstract
In this week's BMJ, Peter Bacchetti complains about another side of peer review: finding errors when they are not there (p 1271). All those involved in peer review certainly know about the shortcomings of the process, but I am not so sure if the majority of readers are so aware of these shortcomings. Often I hear such questions as “hasn't this article gone through peer review?” A visit to www.ama-assn.org/public/peer/peerhome.htm might serve as an eye opener. The Journal of the American Medical Association and the BMJ Publishing Group organise a conference on peer review every four years—it's a bit like a biomedical publishing version of the Olympic Games. Unfortunately, the conference website is not very entertaining—the pages mainly consist of text as the topic doesn't lend itself to interactivity. You might find, however, interesting information on what it could mean to be a second, third, or last author, or what makes a good referee. The peer review process appears to be a threatening and rather intangible animal to many journal contributors. Accordingly, the Committee on Publication Ethics (COPE, www.publicationethics.org.uk) states that all scientific journals should be explicit about their peer review process. Now comes the interactive part: try to find a description of the peer review process of your favourite journals. I am not sure whether this will come as a surprise but if most of the major general and specialist journals mention their peer review process at all, they do so only in vague terms. Those that are explicit about it include The Annals of Internal Medicine (www.annals.org/shared/author_info.html) and the Canadian Medical Association Journal (www.cmaj.ca/misc/ifora.shtml#rev), not forgetting the BMJ (bmj.com/advice/30.html). Funding bodies such as the Medical Research Council (www.mrc.ac.uk/index/funding/funding-specific_schemes/funding-assessment_process.htm) and the United States' National Institutes of Health (grants1.nih.gov/grants/peer/peer.htm) provide even more detailed information. After struggling your way through all this information it still might not be terribly clear what will actually happen to your paper. In the case of open peer review, you know at least afterwards what happened to your paper, but there are certainly other good reasons for opening the process (bmj.com/cgi/content/full/318/7175/4).
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 | 0.095 | 0.282 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.028 | 0.016 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.153 | 0.263 |
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".