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Record W4402692262 · doi:10.1177/15274764241280636

“Every Time I Move My Arm, it Costs the Cartoon Network 42 Bucks”: Remixing Limited Animation in <i>Space Ghost: Coast to Coast</i>

2024· article· en· W4402692262 on OpenAlexfundno aff
Jacqueline Ristola

Bibliographic record

VenueTelevision & New Media · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsnot available
FundersFonds de Recherche du Québec-Société et CultureUniversity of Oxford
KeywordsAnimationSpace (punctuation)Broadcasting (networking)Computer scienceReuseProduction (economics)Channel (broadcasting)Block (permutation group theory)Visual artsTelecommunicationsComputer graphics (images)MultimediaArtEngineeringEconomics

Abstract

fetched live from OpenAlex

This article examines how media production is shaped under media conglomeration through a close analysis of Space Ghost: Coast to Coast (1994–2008), arguing that animation evinces the corporate strategy of archive reuse through its esthetics. It compares how the limited animation of Hanna-Barbera Productions transformed in the shift to cable and greater media conglomeration under Turner Broadcasting System. Using a thick description of the production process, the chapter illustrates how Cartoon Network programmers remixed the corporate archive to create Space Ghost: Coast to Coast . Through this remixed production process, channel programmers used limited animation esthetics to disclose on their own labor as programmers and producers within Turner’s media empire. It ends by examining how Space Ghost: Coast to Coast ’s unique production techniques and esthetics shaped Cartoon Network’s adult programing block, [adult swim].

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)0.003
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 score0.023
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.008
Scholarly communication0.0050.004
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.001

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 head0.029
GPT teacher head0.300
Teacher spread0.270 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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