Bibliographic record
Abstract
eer-reviewed publication is a vital step in the research process and permits research findings to be communicated effectively to readers.The peer review process is designed to select work of relevance to particular audiences, to improve the quality of reporting, and thus increase its transparency (eg, allowing methods to be replicated) [1].Although it is by no means perfect, there is some evidence that peer review performs these functions, or at least that the quality of articles tends to improve from submission to publication [2,3].However, peer review cannot, by itself, prevent fraud or misconduct, although in some cases it may help detect them.The publication process is therefore based on a degree of trust in the honesty and intentions of authors, reviewers, and editors.However, responsible conduct in publishing research is not always fully understood, and while egregious behaviour (such as copying a published article into a new document and submitting it to another journal with new author names) is easily recognised as misconduct (in this case, pla-Why do we need international standards on responsible research publication for authors and editors?Elizabeth Wager 1 , Sabine Kleinert 2 giarism), other practices may be harder to classify.Without a good understanding of the ethics and conventions of publication, it is possible for authors and editors to unwittingly overstep the mark and do something that others find unacceptable [4].It is therefore helpful for journals and institutions to provide clear guidance for authors and editors on what is expected of them.Delivering the best possible healthcare requires a reliable evidence-base of research publications.Both authors and editors have responsibilities when publishing research yet it can be hard to find guidance on these.Most journal instructions concentrate on style and formatting but give little or no information about research and publication ethics.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.186 | 0.136 |
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".