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Record W4308989110 · doi:10.3138/utq.91.4.04

The Culture of Poetics and the Poetics of Culture of Marshall McLuhan – Toronto and Canada, Text and Context

2022· article· en· W4308989110 on OpenAlexaffvenueabout
Jonathan Locke Hart

Bibliographic record

VenueUniversity of Toronto Quarterly · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Communication, and Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPoeticsPoetryLiteratureContext (archaeology)Identity (music)SociologyArtArt historyHistoryAestheticsArchaeology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.219
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes3
Has abstractyes

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