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Record W2154792643 · doi:10.1002/asi.22840

The impact of technological advances on recording studio practices

2013· article· en· W2154792643 on OpenAlexaff
Amandine Pras, Catherine Guastavino, Maryse Lavoie

Bibliographic record

VenueJournal of the American Society for Information Science and Technology · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsUniversité de MontréalMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsStudioMusicalVariety (cybernetics)Scope (computer science)TRACE (psycholinguistics)Music industrySound recording and reproductionThe InternetComputer scienceVisual artsManagementTelecommunicationsArtEconomicsWorld Wide Web

Abstract

fetched live from OpenAlex

Since the invention of sound reproduction in the late 19th century, studio practices in musical recording evolved in parallel with technological improvements. Recently, digital technology and Internet file sharing led to the delocalization of professional recording studios and the decline of traditional record companies. A direct consequence of this new paradigm is that studio professions found themselves in a transitional phase, needing to be reinvented. To understand the scope of these recent technological advances, we first offer an overview of musical recording culture and history and show how studio recordings became a sophisticated form of musical artwork that differed from concert representations. We then trace the economic evolution of the recording industry through technological advances and present positive and negative impacts of the decline of the traditional business model on studio practices and professions. Finally, we report findings from interviews with six world‐renowned record producers reflecting on their recording approaches, the impact of recent technological advances on their careers, and the future of their profession. Interviewees appreciate working on a wider variety of projects than they have in the past, but they all discuss trade‐offs between artistic expectations and budget constraints in the current paradigm. Our investigations converge to show that studio professionals have adjusted their working settings to the new economic situation, although they still rely on the same aesthetic approaches as in the traditional business model to produce musical recordings.

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 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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.998

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 studies0.0010.005
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.304
Teacher spread0.278 · 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 teacher head, not a consensus.

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

Citations45
Published2013
Admission routes1
Has abstractyes

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