Open educational resources and the evolving value chain of education in developing countries
Bibliographic record
Abstract
Open educational resources (OER), in combination with emerging information and communication technologies (ICT), may enable opportunities for education in regions of the world where few opportunities exist today. There are growing literatures examining the underlying technology and the practice of open education, but the social implications of OER for groups and communities in developing countries have received comparably less attention. This paper seeks to better understand the social impact of OER by systematically applying two theoretical frameworks from the management literature on technological innovation. First, we assess the influence of nonmarket forces on the motivation and ability for OER innovation. Second, we examine the evolving value chain of education in developing countries. We find that OER are causing some participants in the value chain to change their roles, allowing others to shorten distances and bypass barriers, and enabling new participants that did not exist before. For developing countries, these changes provide opportunities to enhance access to education resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".