The oligopoly's shift to open access publishing: How for-profit publishers benefit from gold and hybrid article processing charges
Bibliographic record
Abstract
This presentation shares findings from a study that estimates fees paid for gold and hybrid open access articles in journals published by the oligopoly of academic publishers, which acknowledge funding from the Canadian Tri-Agency. It employs bibliometric methods using data from Web of Science, Unpaywall, open datasets of article processing charges list prices as well as historical fees retrieved via the Internet Archive Wayback Machine for journals published by Elsevier, Springer-Nature, Wiley, Sage and Taylor & Francis to estimate article processing charges for open access articles published between 2015 and 2018 that acknowledge funding from the Canadian Federal funding agencies CIHR, NSERC, and SSHRC, as well as grants jointly administered by the Tri-Agency. During the four-year period analyzed, a total of 6,892 gold and 4,097 hybrid articles that acknowledge Tri-Agency funding were identified, for which the total list prices amount to $US 27.6 million.
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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".