Inequities of Article Processing Charges: How the Oligopoly of Academic Publishers Profits from Open Access
Bibliographic record
Abstract
Public lecture at the Knowledge Equity and Justice Spring Seminar, 17 May 2022, organized by Stacy Allison-Cassin and SPARC Since the early 2010s, more than half of peer-reviewed journal articles have been published by the so-called oligopoly of academic publishers: Elsevier, SAGE, Springer-Nature, Taylor & Francis and Wiley. These companies make immense profits from publishing scholarly journals, traditionally through subscriptions from academic libraries, the reader pays model. With more and more libraries cancelling so-called ‘Big Deals’, these publishers have expanded their revenues by making authors pay article processing charges (APCs) for open access (OA) publishing. The author-pays model creates inequities and barriers that exclude many from publishing, such as underrepresented groups or researchers from less-resourced countries. This presentation demonstrates the growth of gold and hybrid OA articles published in oligopoly journals indexed in the Web of Science and provides evidence of the amount of APCs paid in Canada and globally. It highlights the inequities of the author-pays model and discusses alternative routes to OA.
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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".