A bibliometric mapping of open educational resources
Bibliographic record
Abstract
Open educational resources (OER) is a topic that has aroused increasing interest by researchers as a powerful contribution to improve the educational system quality and openness, both in face to face and distance education. The goal of this research is to map publications related to OER, dating from 2002 to 2013, and available through the Web of Science and Scopus scientific databases as well as in the OER Knowledge Cloud open repository. Data were used to explore relevant aspects related to the scientific production in OER, such as: (i) number of publications per year; (ii) most cited publications; (iii) authors with higher number of publications; (iv) institutions and countries with more publications and (v) most referenced bibliography by the authors. The analysis has included 544 papers, written by 843 authors, from 338 institutions, from 61 different countries. Moreover, the analysis has included the publications referenced and the author’s keywords, considering 6,355 different publications and 929 different keywords. Besides presenting a bibliographic mapping of the research on OER, this paper also intends to contribute to consolidate the idea that OER is a promising field for researchers, in line with the spreading of the Open movement.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.188 | 0.237 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".