Bibliographic record
Abstract
The spread of the coronavirus (COVID-19) is placing impossible demands on distance education. With the closure of schools and colleges, teachers are being given only weeks to put their courses online regardless of their lack of online experience and support facilities. In the United States of America, international students who fail to continue their studies online have been threatened with expulsion to their own countries, where online resources may be unavailable. The failure of institutions to place their curricula online efficiently will be a public relations disaster blamed not on those who have issued these impossible demands but on the false premise that distance education methods were ineffective all along. The article summarizes the problems facing teachers and students in this situation, and repeats a conclusion expressed by me in previous reflection articles: that the surest way to make online learning effective is to consult the decades of practical experience in the distance education literature.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".