The Future of Scientific Publishing
Bibliographic record
Abstract
Presentation at the Royal Society of Canada Celebration of Excellence & Engagement 2025 panel "The Future of Scientific organized by Érudit Panel discussion with Chad Gaffield, Arash Abizadeh and Stefanie Haustein, 15 November 2025, Montréal Researchers find themselves trapped in a publishing system under strain: libraries can no longer afford rising subscription costs, journals have gone digital but retained outdated print-era conventions, and the pressure to publish in prestigious venues continues to shape careers and research priorities. Today, the system faces a deeper crisis of credibility: paper mills and AI tools fabricate fake articles, while editors struggle to find reviewers as submissions soar. As commercial publishers profit from open access policies through fees that are based on prestige not production costs, pressure on editors to publish more, faster, and on marketable topics continues to grow. However, the call for reform has never been louder, with the National Institutes of Health (NIH) in the U.S. considering caps on article processing charges (APCs) and the Royal Society in the U.K. adopting a collectively funded diamond open access model. With an updated Tri-Agency open access policy about to be released, the panel will discuss what role Canada, and foremost Royal Society members, can play in reclaiming scholarly publishing as a trustworthy, sustainable, and community-driven public good.
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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 | 0.037 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.060 | 0.026 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.064 | 0.052 |
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".