Artur Skweres, McLuhan’s Galaxies: Science Fiction Film Aesthetics in Light of Marshall McLuhan’s Thought, Cham: Springer, 2019, pp 110
Bibliographic record
Abstract
The first association that may come to mind when we juxtapose Marshall Mcluhan's media theory and the movies is one of the scenes from Annie Hall, a Woody allen film from the late 1970s.In this scene, the protagonists, played by the film's director and Diane Keaton, are standing in line at the box office next to a man who is excitedly telling his partner about a theory of the Canadian thinker.When allen interrupts this particularly annoying monologue and accuses him of completely misrepresenting Mcluhan's theses, the outraged man announces that he knows what he is talking about because he teaches a course on television, media, and culture at Columbia university in New York.at that moment, quite unexpectedly, of course, the professor from Toronto appears in the cinema hall in person and -with characteristic self-mockery -says to the astonished academic: "I heard what you were saying.You know nothing of my work.You mean my whole fallacy is wrong.How you ever got to teach a course in anything is totally amazing" [Annie Hall, youtube.com].Fortunately, this snappy remark from the Canadian thinker did not stop researchers from all over the world from making more, often very original attempts to interpret and apply his theories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".