Towards Sustainable and Coordinated Methods for Estimating Open Access Costs at Canadian Higher Education Institutions
Bibliographic record
Abstract
Higher education institutions in Canada aim to provide access to knowledge through subscriptions and investments in OA publishing. As subscription and OA publication costs continue to increase, some institutions have established funds to support authors' payment of publication fees. Others have adopted models such as read-and-publish deals or "transformative agreements", where institutions pay publishers a lump sum for subscriptions and publishing fees for authors. As institutions continue to subscribe to journals, support authors with OA publishing, and negotiate agreements, accurately estimating institutional OA spending is imperative to determining the cost effectiveness of deals and necessary funding support for authors. Methods to estimate OA costs have largely developed in silos across the country. This commentary presents observations derived from work performed across Canada on the challenges accompanying OA estimation and calls for a more coordinated approach to establish standardized, sustainable methods. Calibrating efforts across institutions can support the development of reliable methodologies and streamline resources helpful for the more efficient performance of the often onerous task of estimating costs.
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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 | 0.038 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".