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De la televisión a la televisión extendida a partir de Marshall Mcluhan

2016· article· es· W2955887570 on OpenAlexaboutno aff

Bibliographic record

VenueConsensus · 2016
Typearticle
Languagees
FieldComputer Science
TopicMedia and Digital Communication
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanitiesPolitical science

Abstract

fetched live from OpenAlex

This text is composed of 4 sections. In the first one, I recovered the emotive speech made by WilburSchramm about the first television broadcasting carried out on January 26th 1926 by John Logie Bairdin order to support the relevance of the term “Age of television”. The television is actually considered asthe emblematic media in the “Age of electricity”. In the second section, I studied the thesis developed byMarshall McLuhan about the so-called “shy giant”. At the beginning of 1960s, the television captivatedMarshall McLuhan. From a particular ecumenical perspective, the Canadian researcher believed thatthe television would restore the sensitive balance, thus favoring the transition to the “global village”. Asthe years went by, his optimism about the television started to vanish, and McLuhan adopted a morecritical position, similar to Neil Postman’s, against the television. In the third section, I stood out thatMcLuhan anticipated the media convergence; then, I explained his Tetrada and pointed out the wayBob Logan (2010) used it to make an analysis of the television and the cable television. Finally, in thefourth section, I recovered the Tetrada from McLuhan to study some of the most interesting modalitiesof extended television: Youtube, Netflix and Internet Protocol Television (IPTV).

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)0.001
metaresearch head score (Gemma)0.002
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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0110.003

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 head0.020
GPT teacher head0.289
Teacher spread0.269 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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