Bibliographic record
Abstract
As we continue to celebrate IRRODL's remarkable journey, this issue exemplifies our journal's enduring commitment to advancing open and distributed learning through rigorous scholarship and global collaboration.Detailed in "Twenty-five years of innovation and knowledge sharing: the legacy and future of the international review of research in open and distributed learning," which opened our first 2025 issue, since founding in 2000 as a diamond open access journal, free to both readers and authors, IRRODL has remained steadfast in publishing high-quality research while maintaining international diversity in authorship, readership, and editorial contributions.Our accessibility and innovation have resulted in over 13.5 million downloads and 4 million unique visitors since 2011, with high citation rates and steady impact factor growth reinforcing our advocacy for diamond open access and inclusive scholarship.With authors from 31 countries contributing to recent volumes (2023-2024), we maintain strong North American and UK representation, while 64.3% of our readers come from the Global South, demonstrating our international relevance.This global reach reflects IRRODL's position as the most cited Canadian education journal and our ranking among the top 20 educational technology journals worldwide.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.116 | 0.083 |
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".