Open distance learning for development: Lessons from strengthening research capacity on gender, crisis prevention, and recovery
Bibliographic record
Abstract
This paper documents the experience and lessons from implementing an e-learning program aimed at creating research capacity for gender, crisis prevention, and recovery. It presents a case study of bringing together a multidisciplinary group of women professionals through both online and face-to-face interactions to learn the skills needed to be a successful researcher. It reviews the issues related to distance learning programs with particular reference to the e-learning courses and highlights the constraints and challenges in implementing them. Lessons from the experience for future development of similar courses indicate that participant profiling prior to the course, user friendliness of technology, meeting various learning styles, encouraging and rewarding online exchanges, commitment of course moderators, a variety of learning materials, and mixed approaches to learning are some of the factors that can enhance the success of e-learning programs. The paper concludes that enhancing skills of developing country researchers through e-learning programs can increase learning accessibility to those living and working in remote and conflict ridden areas, and bring together a network of professionals to interact and exchange experiences on common problems and solutions.
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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 | 0.030 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".