Bibliographic record
Abstract
The bicentenary in 2011 of the Luddite Revolt prompts us to ask ‘what would Ned Ludd think of today’s automated styles of distance education?’ He would no doubt echo the common criticism that educational technologies create an impersonal style of teaching and learning, and devalue the teacher. He would probably agree that online methods have major potential for millions of distance‐based students who cannot attend classroom‐based education and training; but he would emphasise the need for quality assurance and cost‐effectiveness studies in distance education implementation. He might also ask why anyone would encourage the development of e‐learning in countries where the Internet is largely inaccessible. This article uses the Luddites’ views of workplace automation to explore how global distance education practices might be improved. It suggests that the intentions of the original Luddites were laudable and worthy of application in distance education today.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".