Bibliographic record
Abstract
These are materials associated with the roundtable event, "Publisher bans & DORA". The roundtable was held as part of a week of events commenorating the tenth anniversary of the Declaraion on Research Assessment. The event description read, "Several publishers – particularly those specializing in open access journals – have rapidly increased the number of articles submitted to, and published by, their journals. But despite their popularity with researchers, some institutions are refusing to consider papers published in some journals for assessment purposes. This event will examine whether such bans are consistent with DORA and academic freedom." The roundtable was held 15 May 2023 14:00 (UTC), both online and in person at McMaster University in Canada. Version 1.0: PDF based on introductory comments to roundtable. Version 2.0: Added video of roundtable recorded on Zoom. This video is also available on YouTube: https://youtu.be/LU2c3J9X--c Version 2.1: Updated PDF. Added one more case that was found a few days after the roundtable. Version 3.0: Added PDF of transcript. Version 4.0: Added PDF of comments.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.819 | 0.655 |
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".