Bibliographic record
Abstract
I am delighted to write this editorial for the inaugural issue of E-Learning. It is an exciting first issue with articles from a range of experts who analyse and discuss in critical and constructive terms some fundamental aspects of elearning – a concept whose time has come. Yet it has passed almost silently into the language of education without much critical thought. It is as though the addition of ‘e’ – with a hyphen – indicates simply a change of medium as though it was ‘business as usual’, except we experience the substitution of an electronic medium for classroom ‘talk’ or other structured educational media instruction. It was Marshall McLuhan, the Canadian media philosopher, who first taught us to look at the deep structure of media when he stated ‘the medium is the message’, the title of his famous book, later changing it to The Medium is the Massage (McLuhan, 1967). McLuhan, we must remember was educated at Cambridge by I.A. Richards and schooled on James Joyce, the symbolist poets and Ezra Pound. In other words, he had a well-developed appreciation of literature and had gained sophisticated knowledge and practice of its tools of analysis as a basis for his critical approach to understanding media.[1] With e-learning, then, we must be willing to recognise the deep structure of the medium and this means, among other things, to learn to become sceptical of histories that are ‘event driven’ or ‘personality driven’ or ‘technology driven’. In conversation with a colleague and friend, Bob Davis at the University of Glasgow, I was recently reminded of Jonathan Swift’s ‘writing machine’ as he sketches it in Book 4 of Gulliver’s Travels:
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.035 |
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".