Measuring the prevalence of open access in Canada: A national comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For two decades, open access (OA) has gained momentum worldwide. However, adoption of OA in Canada is lagging compared with other countries. Using data from Dimensions and Érudit, this paper provides an overview of OA dissemination in Canada, focusing on the effect of institutions, language, and funding. Papers in French, and from Quebec universities, are more likely to be OA, while papers from engineering-oriented institutions are less likely to be OA. Regarding funders, those in health sciences have higher OA compliance. The paper concludes discussing disciplinary differences in OA dissemination, low compliance to OA mandates in Canada, and the role of Érudit.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | BibliometricsOpen science Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | low |
| gpt | BibliometricsOpen scienceScholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Other design | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.063 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it