Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Distance learning accelerated and diversified during the Covid-19 pandemic, with the result that individual teachers working with their normal classroom groups now account for most of the courses offered online. However, this provision of “closed distance learning” will not suffice for the needs of the hundreds of millions of people who will seek secondary schooling, degree studies, and continuing education in the next 20 years. We describe how open distance learning can be conducted at scale through open universities, open schools, and MOOCs, which are all designed to cope with mass demand. Our focus is on how these organizations are run. This embraces institutional design and organization, governance, management and administration, and leadership. The three types of providers have various corporate and governance structures: public open universities, open schools under the aegis of government, and commercial MOOCs companies. However, the challenges of management and administration, which are to sustain operations at scale around the clock worldwide, are rather similar. Their leadership requires a genuine commitment to serving the disadvantaged, an ability to secure the trust of governments, understanding of the opportunities that emerging technology offers for distance education, and thorough familiarity with the institutional dynamics of open and distance teaching and learning systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 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 it