Standardizing Preprint Policies Are Needed to Clarify What Counts as a Prior Publication
Bibliographic record
Abstract
Preprints allow authors to rapidly disseminate their findings or ideas and can serve as a useful prelude to the submission to a peer-reviewed journal, especially in situations in which peer review might take weeks or months to complete and early exposure of information to other academics may be beneficial to them and the peer community, especially in terms of feedback, while serving as a form of early intellectual recognition. Despite these positive aspects, there is still considerable heterogeneity among policies, loose wording, ambiguity within preprint policies, and even contrasting policies among journals within the same publisher. This article presents several discussion points related to preprint servers and publishers that would allow them to improve their preprint-related author-based services and policies, especially those pertaining to what constitutes a ‘prior publication.’ Although generative artificial intelligence is not an authoritative or scientific source, the authors also sought guidance by using ChatGPT-4 to expand their discussion.
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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 | 0.254 | 0.538 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.044 | 0.043 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.017 | 0.028 |
| Insufficient payload (model declined to judge) | 0.017 | 0.024 |
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".