Bibliographic record
Abstract
To close out 2024, this issue of IRRODL offers eight research articles, a book note, two literature reviews, and a leadership note in open and distance learning, all of which contribute to the ongoing changes within this field of education.The IRRODL editors wish to thank our readers, reviewers, and authors for their continued support of the journal and wish that 2025 may provide further opportunities for learning from and with each other in the areas of open and distributed learning."Strengthening policies for education, innovation, and digitization through teacher training: Evaluating ProFuturo's open model in Ecuador" by the researchers Hernández-Sellés and Massigoge-Galbis provide findings from a 2020-2022 study.Through a collaboration between the Ecuadorian Ministry of Education and the ProFuturo program over 7200 primary and secondary school teachers received training to strengthen digital competency among teachers and their pupils.The research also explored the strengthening of mass ICT training for teachers within Ecuador.Bardakcı sought to understand quality assurance in open and distance education through an examination of published papers."Unveiling scholarly insights: Quality assurance in open and distance education" provides the results of this recent study.The findings indicate that the scholarship continues to expand with
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.202 | 0.133 |
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".