The Culture of Poetics and the Poetics of Culture of Marshall McLuhan – Toronto and Canada, Text and Context
Bibliographic record
Abstract
This article discusses Marshall McLuhan’s idea of Canada and his work on poetry, poetics, humanities, and related subjects. McLuhan’s sense of poetry, poetics, technology, culture, and nature depends in part upon his view of Canada. He sees the connections between the United States and Canada while also admitting some distinctions. Britain, France, Canada, and the United States are interrelated in the forging of boundaries and identities. The article assumes that McLuhan’s contribution to the long-time debate on Canadian identity is thoughtful, poetic, far-reaching, and deserving of detailed attention. The figure of the artist is important for McLuhan, who says that the electric age should be the opposite of surface and requires more thought and work. Discussing poetry, McLuhan examines Ezra Pound, who wrote notes to T.S. Eliot’s The Waste Land. McLuhan provides notes or glosses to his own work in his poetic vision in The Gutenberg Galaxy, and his culture of poetics and poetics of culture were of Toronto, the University of Toronto, Canada, North America, and beyond. For McLuhan, the electric age gives Canada – a borderline case – advantages and provides new ways of thinking.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.030 | 0.039 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".