Collaboration networks and their effects on open access uptake: Analyzing how authors of a Canadian federal science department comply with mandates
Bibliographic record
Abstract
This research-in-progress paper presents the results of an exploratory analysis investigating the effect of institutional collaborations on compliance with mandated open access (OA) policies by Canadian federal science-based departments. More specifically, we explore how authors affiliated with Agriculture & Agri Food Canada (AAFC) follow the Canadian government's Roadmap for Open Science to make publications OA (gold, hybrid, green) when publishing with or without national and international collaborators. Using VOSviewer, collaboration patterns and OA compliance is visualized. Preliminary findings show that AAFC complies less with the federal OA mandate when publishing alone or with co-authors from academia and OA uptake is highest when they collaborate with other government partners.
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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 | 0.033 | 0.210 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.031 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".