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Modernism’s “Doors of Perception”: From Ezra Pound’s Ideogrammic Method to Marshall Mcluhan’s “Mosaic”

2019· article· en· W3004380900 on OpenAlexaff
Panayiotes T. Tryphonopoulos, Demetres P. Tryphonopoulos

Bibliographic record

VenueLiterature of the Americas · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Communication, and Education
Canadian institutionsUniversity of AlbertaQueen's University
Fundersnot available
KeywordsPound (networking)Modernism (music)ArtArt historyPhilosophyLiteratureComputer science

Abstract

fetched live from OpenAlex

Writing to Ezra Pound in May 1948, Marshall McLuhan told the poet that, "we [that is, Hugh Kenner and I] have long taken a serious interest [in] your work."And so, McLuhan along with Kenner visited Pound at St Elizabeths in June 1948.The rest makes for interesting modernist literary and cultural studies history.This paper argues that McLuhan's method of composition, which he called a "mosaic," derives from his understanding of Pound's poetics of the ideogrammic method.In The Gutenberg Galaxy: The Making of Typographic Man (1962), McLuhan explains that the book "develops a mosaic or field approach to its problems.Such a mosaic image of numerous data and quotations in evidence offers the only practical means of revealing the causal operations in history."McLuhan learned much from the American poet, including to view literature/pedagogy as "training of perception"; and both developed texts that placed readers in media res, encouraging an heuristic approach to "reading" whereby readers are empowered to arrive at their own meaning or interpretation irrespective of the writers' ideology and/or agenda.Using examples from The Cantos and The Medium is the Massage: An Inventory of Effects (1967), this essay also probes the relationships between modernist aesthetics, technological prophesy and sociopolitical praxis.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.053
Scholarly communication0.0090.012
Open science0.0010.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.343
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2019
Admission routes1
Has abstractyes

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