Bibliographic record
Abstract
Enthusiasts and evangelists of open educational resources (OER) see these resources as a panacea for all of the problems of education. However, despite its promises, their adoption in educational institutions is slow. There are many barriers to the adoption of OER, and many are from within the community of OER advocates. This commentary calls for a wider discussion to remove these barriers to mainstreaming OER in teaching and learning and argues for a rethinking of the idea of ‘open’ to make it more inclusive by redefining the concept. It reminds us of the original thinking behind OER – which was to create universally available educational resources that can improve the quality of teaching and learning. This commentary posits arguments against conflating OER and open education, questions the narrow definitions of OER, and raises issues around how to be more flexible and open to mainstreaming OER and removing barriers from within the OER movement.
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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.019 | 0.050 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".