Bibliographic record
Abstract
Conceptualized as open educational resources (OER) in 2002, the field has a relatively short history. During this period, there have been some significant developments, including the availability of more resources and research publications (including literature reviews) in peer-reviewed journals and conferences. This review of reviews adopted a combined approach of tertiary review and integrative review to analyze the corpus of data collected from SCOPUS, Web of Science, Academic Premier, and Google Scholar. In all, 42 reviews on OER could be identified for the analysis, which indicated that most of these are published as journal articles, with only five published in conferences. Journal articles are published in 29 unique titles, with the International Review of Research in Open and Distributed Learning at the top, followed by Sustainability. 62% of these are available in open access, with most being systematic reviews. The field demonstrates high research collaboration with multiple authors in most reviews. The average quality rating of the reviews is low. Most of the reviews are published by authors from the USA, while researchers from Anadolu University and Beijing Normal University are top contributors. The thematic analysis using UNESCO’s recommendation on OER as a framework indicates research gaps in the areas of sustainability and international cooperation. The study concludes with a set of guidelines to promote effective OER implementation.
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.
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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".