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
Following the burst of the dot-com bubble in 2000, scepticism about e-learning replaced over-enthusiasm. Rhetoric aside, where do we stand? Why and how do different kinds of tertiary education institutions engage in e-learning? What do institutions perceive to be the pedagogic impact of e-learning in its different forms? How do institutions understand the costs of e-learning? How might e-learning impact staffing and staff development? This book addresses these and many other questions. The study is based on a qualitative survey of practices and strategies carried out by the OECD Centre for Educational Research and Innovation (CERI) at 19 tertiary education institutions from 11 OECD member countries â Australia, Canada, France, Germany, Japan, Mexico, New Zealand, Spain, Switzerland, the United Kingdom and the United States â and 2 non-member countries â Brazil and Thailand. This qualitative survey is complemented by the findings of a quantitative survey of e-learning in tertiary education carried out in 2004 by the Observatory on Borderless Higher Education (OBHE) in some Commonwealth countries.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it