Bibliometric Insights Into the Open Education Landscape
Bibliographic record
Abstract
This bibliometric analysis explores the rapidly growing field of open education, offering insight into its nature and the wide range of academic topics it covers. This study applies co-citation and co-word analyses approach to critically review 402 publications from the Web of Science database. The aim is to identify emerging topics, seminal works, and dominant trends in the literature on open education. The co-citation analysis identifies key publications and thematic clusters that define the field, including discussions on pedagogical innovations, equity and accessibility, quality assurance, and the global impact of open educational practices (OEP). Co-word analysis, on the other hand, highlights the recurrent and emerging keywords within the literature, revealing focal points such as digital transformation in education, the role of massive open online courses (MOOCs), and the significance of open educational resources (OER) in fostering inclusive and equitable learning environments. This study stands out for its quantitative approach in mapping the current academic conditions of open education, offering insights into the dynamic interplay between technology, policy, and pedagogy. It emphasizes the need for a collaborative, inclusive approach to education, employing open educational resources and methods to fulfill the different needs of learners globally. Through this analysis, the study contributes to a deeper understanding of the current state and future directions of open education, advocating for policies and practices that support sustainable, accessible, and high-quality educational experiences.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | BibliometricsOpen science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.021 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.240 | 0.311 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.001 | 0.006 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".