Publication History: A Double-DOI-Based Method for Storing and/or Monitoring Information about Published and Corrected Academic Literature
Bibliographic record
Abstract
The status of published literature can change at any time in its history following publication, although existing structures in academic publishing for recording these changes, despite the existence of a number of robust tools—such as the digital object identifier (DOI)—appear to be insufficiently robust, or used too inconsistently or inefficiently, to deal with multiple corrections. In this article, an information storage method and corrective measure or tool is proposed—the ‘publication history’—that considers the full history and background of an article’s publication. The ‘publication history’ is adjusted to record changes to an article over time, and is thus a ‘live’ document, always open to modification and updating. The ‘publication history’ has the potential to accommodate, in a single document (in both PDF and HTML format), information about pre-publication (e.g., preprints) and post-publication events, including submission, resubmission, acceptance date, handling editors, peer-review format, corrections, expressions of concern, and retractions. The ‘publication history’ employs two DOIs, one for the article and one for any and all edits, to document these changes. Our proposal offers one possible solution for fortifying the integrity of peer review and the publication process pre- and post–peer review. The double-DOI-based ‘publication history’ can be applied to any document.
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 | 0.050 | 0.239 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.035 | 0.028 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.113 | 0.124 |
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".