The publication facts label: A public and professional guide for research articles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Key points The expansion of open access entails a responsibility for supporting this public access with a guide to why, in an ‘Age of Misinformation’, research may be trustworthy. Such a guide can also provide a check on predatory journals, a fear of which may be unduly limiting researchers tapping into the expanding global scale of research activity. Journal publishing platforms offer opportunities for automating the gathering and presenting of relevant data for assessing journal adherence to scholarly standards. Industry organizations, such as ORCID, Crossref, and DOAJ, offer trust and verification networks that can be employed to further strengthen scholarly publishing integrity.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.140 | 0.426 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.026 | 0.117 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.143 | 0.009 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it