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Record W3200263860 · doi:10.5210/spir.v2021i0.12161

RIP IT UP AND START AGAIN: REMIX AND CO-OPTION IN THE MEDIA INDUSTRY

2021· article· en· W3200263860 on OpenAlexaff
Alessandro Delfanti, Michelle Phan

Bibliographic record

VenueAoIR Selected Papers of Internet Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMainstreamCommodificationNewspaperAdvertisingChurningMedia studiesMultimediaPolitical scienceSociologyBusinessComputer scienceLaw

Abstract

fetched live from OpenAlex

Media workers use radical remix techniques to produce content for the mainstream media industries. Rather than thinking of piracy as a form of resistance, we identify these practices of remix labor as a renewed commodification of media piracy and remix cultures (Johns 2009; Mueller 2019). Be it wanna-be influencers remaking the latest viral video on TikTok, videomakers producing dozens of rip-o-matic clips that will constitute the storyboard for TV ads, or writers churning out pieces in content farms in the hope they land on a newspaper, the media industry outsources work to and exploits masses of people who use piracy and remix to produce content based on scissor reels composed from existing audio, textual and visual materials.

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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0090.025
Scholarly communication0.0170.021
Open science0.0010.010
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0140.001

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 head0.078
GPT teacher head0.314
Teacher spread0.236 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

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