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Record W1854869513 · doi:10.7560/vlt7604

Pro Tools, <i>Playback</i>, and the Value of Postproduction Sound Labor in Canada

2015· article· en· W1854869513 on OpenAlexaboutno aff
Katherine Quanz

Bibliographic record

VenueThe Velvet Light Trap · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsSound (geography)WorkstationOrder (exchange)Sound recording and reproductionScholarshipRhetoricProduction (economics)Digital audioDigital scholarshipMultimediaEngineeringComputer scienceDigital signal processingBusinessPolitical scienceManagementElectrical engineeringAcousticsWorld Wide WebAudio signalEconomics

Abstract

fetched live from OpenAlex

The motion picture industry’s transition from magnetic tape–based sound editing to digital, computer-based sound editing occurred from the mid-1990s through the early 2000s. This transition had major consequences on postproduction sound labor in Canada. In this article, I argue that digital audio workstations, such as Digidesign’s Pro Tools, threatened the role of Canadian postproduction sound practitioners. In order to protect their position within the industry, they adopted a rhetoric of selfpromotion to change the value of their profession from technician to artisan. In order to make this claim, I analyze the discourse of sound editors and rerecording engineers in Canada’s dominant trade paper, Playback, over an eighteen-year period. This research contributes to ongoing scholarship on the role of digital audio workstations in film soundtrack production, as well as the impact of digital technologies on media industries and production cultures.

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)0.003
metaresearch head score (Gemma)0.007
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: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.975

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0300.017
Scholarly communication0.0130.002
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.058
GPT teacher head0.261
Teacher spread0.203 · 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
Published2015
Admission routes1
Has abstractyes

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