Bibliographic record
Abstract
In the many conversations around or about Marshall McLuhan, what often gets lost or ignored is the fact that for his entire career he was an English professor: a teacher of literature and especially poetry. He came by it honestly, for his mother Elsie McLuhan was an elocutionist who performed dramatic monologues on stage, and although he began university in a mechanical engineering programme, during the summer between his first and second years he ‘read himself into English’ at night after long days as a rodman on a survey crew in the Manitoba north. Following that summer working in what cannot have been pleasant conditions, he left engineering for the arts programme at the University of Manitoba. It is hard to say whether he fell in love with literature or out of love with engineering – possibly it was a bit of both. Regardless, Marshall took a turn for the verse. Is the world better for it? Again, hard to say. Had his mind and energies been applied to the field of mechanical engineering for the following decades, who knows what contribution he would have made? What we can say with a fair degree of certainty is that had Marshall not majored in English Literature, he would not have learned the techniques of literary criticism that he eventually applied beyond literature to culture and technology – with world-changing results.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.192 | 0.076 |
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".