Bibliographic record
Abstract
I will start with a declaration of interest and prejudice. I have been for some thirty years a 'learning technologist', or what used to be unfashionably called an 'educational technologist'. I have observed and read books, chapters and papers by John Daniel, the author of this work, for about twenty years, since his days in Concordia University, Montreal. Professor Daniel is without peer as a judge of continuing, open and distance education. As Vice-Chancellor of the Open University, he has seen his institution grow to a leading position in the UK open-learning market with hundreds of courses offered under its inimitable course-design system. He has ensured that the Open University is a beacon of what is best in innovative teaching and programme delivery. He has steered an adept course, gaining widespread international approval for the prototypical University of the Air. His views are canvassed by the Great and the Good. One can discern his hand in the recent Dealing (1997) recommendations.DOI:10.1080/0968776980060207
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.
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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.033 | 0.043 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".