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Record W4391401971 · doi:10.1177/15274764241227613

Rip It Up and Start Again: Creative Labor and the Industrialization of Remix

2024· article· en· W4391401971 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTelevision & New Media · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsSimon Fraser UniversityUniversity of Toronto
Fundersnot available
KeywordsIndustrialisationCreative industriesMedia studiesSociologyAdvertisingPolitical scienceEconomicsBusinessMarket economyLaw

Abstract

fetched live from OpenAlex

Creative industries rely on workers who use sampling and remix to produce new content assembled from existing materials. In the process, remix cultures are commodified and reshaped by industrial logics. Rip-o-matic videos provide an example. These scissor reels are used as visual storyboards for television commercials. They are produced by video editors who cut and paste clips found on video sharing platforms. Interviews with rip-o-matic producers show the impact of the industrialization of remix on creative workers who face challenges to their ability to assert their creativity, content ownership, and reputation. Other examples, such as social media and fast fashion, nuance the picture. Industrialization also paves the way for automation by generative “AI.” These software tools are based on processes of appropriation and remix that mirror those used by rip-o-matic producers. Remix is in sum at the center of today’s corporate cultural production.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.315
Teacher spread0.248 · 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