A roadmap for offering MOOC from an LMIC institution
Bibliographic record
Abstract
MOOCs are massive open online courses that are globally accessible, free of charge. Given their cost-free and open accessibility, it is surprising that only a few institutions have offered MOOCs from low- and middle-income countries (LMICs). Pakistan recently made this short list of LMICs as the first two MOOCs were launched from the country, in 2014 and 2016. Drawing from that experience, the organizers of that course present a roadmap for LMIC institutions for developing a MOOC, focusing especially on the technological and pedagogical limitations that an LMIC institution might find deterring.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.096 | 0.039 |
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".