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Record W4236033566 · doi:10.4324/9781315654195-14

Herbert Marshall McLuhan: Before Th e Mechanical Bride – David Staines

2015· book-chapter· en· W4236033566 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMedia, Communication, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsBrideArtArt historyPhilosophySociology

Abstract

fetched live from OpenAlex

Marshall McLuhan, Canada&s;s pre-eminent media-theory explorer, raising and addressing historical and contemporary issues of and relationships among media and technology, society and culture, and approached his material and his audience not only as a professor of English literature but also as a modernist literary artist/performer. During the late 1920s and early 1930s, a number of high-modernist writers constructed their interpretations of what were for them visible and identifiable features in the social and physical behaviour of moviegoers. In an art-house lobby such as the New Yorker&s;s cinephiles and aficionados and other movie enthusiasts participate comfortably in what is typically a relaxing atmosphere. This chapter explores McLuhan-at-the-movies within the movie Annie Hall and, in light of literary modernists&s; interest in moviegoing, considers possible interpretations of that historic moment. Woody Allen, on the other hand, loved Bergman, perhaps had even thought of him for what became the &s;McLuhan" role in Annie Hall, just as he had certainly thought of Buñuel.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.284
Threshold uncertainty score0.566

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0190.004

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 head0.090
GPT teacher head0.351
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2015
Admission routes1
Has abstractyes

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