MétaCan
Menu
Back to cohort
Record W2324029037 · doi:10.1177/1527476415577211

Scenes from an Imaginary Country

2015· article· en· W2324029037 on OpenAlexafffund
Dylan Mulvin, Jonathan Sterne

Bibliographic record

VenueTelevision & New Media · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsMcGill University
FundersResearch EnglandSocial Sciences and Humanities Research Council of CanadaMicrosoft Research
KeywordsNTSCThe ImaginaryPerceptionTest (biology)Representation (politics)Computer sciencePoliticsSociologyTelecommunicationsPsychologyLawPolitical scienceHigh-definition television

Abstract

fetched live from OpenAlex

American analog color television—so-called NTSC color—is likely the most pervasive image standard of the twentieth century, yet it is infamous for its technical shortcomings. Through a history and analysis of the National Television System Committee (NTSC) standard, this article argues that the political presuppositions of engineers shaped the representational capacities of television for nearly sixty years. In particular, the test images used to develop a perceptually satisfying image evince assumptions of a leisurely and white United States as the “normal” subject matter of television. In this way, test materials coalesce abstract assumptions about the normal and the exceptional at the level of both form and content. This article concludes that NTSC color served as a model for how normed cultural sensibilities about image quality, perceptual ability, and the representational imaginary have been built into subsequent technical standards. A Scalar version of this paper, with more pictures, is available at http://colortvstandards.net

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0270.002

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 head0.065
GPT teacher head0.266
Teacher spread0.201 · 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
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

Citations24
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueTelevision & New MediaSame topicCinema and Media StudiesFrench-language works237,207