Bibliographic record
Abstract
One truism that scholarship reveals is that the more ambitious and oblique a theorist’s speculations, the more nonsense they will attract from critics. This truth is particularly evident in the case of thinkers who delight in scholarly gamesmanship, who understand, finally, that no degree or clarity of explanation will communicate to others what it took years of intellectual labour to pin down. McLuhan is a case in point, a thinker so fetishized and misunderstood in the symplex of his own time (to borrow Frank Zingrone’s (2001) apt phrase) that he became, in answer, a clown prince in that media’s court. A working model of his own thought, he averred the literate, suddenly arcane, solemnity of his famous colleague Northrop Frye for the dissociative play of the new tribal ethos of an increasingly synthetic age. And he did so, ironically, to achieve much the same end as Frye, who has emerged, over time, as the less conservative of the two. McLuhan’s example of functional satire is what the Democrats in the U.S. have not yet figured out. In order to defamiliarize the hawkish behaviour of their Republican opponents, they should invade a third-world country. What better way to explain violence to a people than to don the coarse ugliness of its rage? It is a strategy McLuhan would have understood.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.083 |
| Scholarly communication | 0.033 | 0.061 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".