Exploration of open educational resources in non-English speaking communities
Bibliographic record
Abstract
Over the last decade, open educational resources (OER) initiatives have created new possibilities for knowledge-sharing practices. This research examines how, where, and when OER are attracting attention in the higher education sector and explores to what extent the OER discussion has moved beyond the English-speaking world. This study analysed English, Spanish, and Portuguese OER queries over a long-term period (2007-2011). The data retrieval was conducted using four online platforms: two academic journal databases (Web of Knowledge and Scopus), one video-sharing Web site (YouTube), and one document-sharing Web site (Scribd). The number (more than 32,860) of search results collected indicate an increasing interest in online OER discussion across languages, particularly outside academic journal databases. Additionally, a widening ‘language gap’ between OER discussions in English and other languages was identified in several platforms. This research reports some of the cultural and language challenges caused by the expansion of the OER discussion and highlights relevant findings in this field.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".