Research On Open Educational Resources For Development In The Global South: Project Landscape
Bibliographic record
Abstract
The Research on Open Educational Resources for Development (ROER4D) project was proposed to investigate in what ways and under what circumstances the adoption of Open Educational Resources (OER) could address the increasing demand for accessible, relevant, high-quality and affordable education in the Global South. The project was originally intended to focus on post-secondary education, but the scope was expanded to include basic education teachers and government funding when it launched in 2013. In 2014, the research agenda was further expanded to include the potential impact of OER adoption and associated Open Educational Practices (OEP). ROER4D was funded by Canada’s International Development Research Centre (IDRC), the UK’s Department for International Development (DFID) and the Open Society Foundations (OFS), and built upon prior research undertaken by a previous IDRC-funded initiative, the PAN Asia Networking Distance and Open Resources Access (PANdora) project. This chapter presents the overall context in which the ROER4D project was located and investigated, drawing attention to the key challenges confronting education in the Global South and citing related studies on how OER can help to address these issues. It provides an abbreviated history of the project and a snapshot of the geographic location of the studies it comprises, the constituent research agendas, the methodologies adopted and the research-participant profile. It also provides an overview of the other 15 chapters in this volume and explains the peer review process.
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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 | 0.051 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".