Bibliographic record
Abstract
he concept of scholarly peer review was introduced in 1665 (more than 350 years ago!) by Henry Oldenburg, the founding editor of the journal, Philosophical Transactions of the Royal Society-a journal that still publishes highquality science today.The peer review process is important for ensuring scientific validity and rigour in published work.This fall, the International Peer Review Congress (held every 4 years) coincided with peer review week and addressed issues as well as innovations in peer review.One of the hot topics discussed was transparency in peer review.This can range from initiatives that involve authors publicly sharing data and materials used in generating their results to publishing reviewer reports and author responses alongside an article.Although there is little agreement in the scientific community about open peer review, it is fair to say there is universal agreement that transparency is a fundamental value of science; the primary purpose of publishing scientific work is to describe what was done and what was found, for the universal good.Reviewers are integral to this process.Predatory publishing is at the other end of the spectrum.In a PEN eNews article, "Don't Fall Prey to Predatory Journals", I describe how these "journals" prey on researchers to submit manuscripts, but fail to adhere to scholarly standards or peer review.A recent commentary in Nature reported that publishing in predatory journals is now a problem in higher income countries [1].In their examination of almost 2000 biomedical articles from 200 predatory journals, 15% of articles were from the United States.Some characteristics of predatory journals include: low article-processing fees (e.g., <$150 USD), a very broad scope, a promise of rapid publication, manuscript submission by e-mail, spelling and grammar errors on the website, and nonprofessional contact e-mail addresses.This suggests an urgent need to stop the submission of manuscripts to these illegitimate publishers of low-quality research.Instead, funders and research institutions should increase funds available for legitimate open-access publication and ensure that researchers are trained in how to select appropriate journals to submit their work.
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 | 0.283 | 0.774 |
| Meta-epidemiology (narrow) | 0.003 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.077 | 0.021 |
| Open science | 0.012 | 0.019 |
| Research integrity | 0.046 | 0.040 |
| Insufficient payload (model declined to judge) | 0.125 | 0.148 |
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".