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Record W3121182847 · doi:10.22215/etd/2015-11068

The "Resort" Studio: An Introduction to the History and Culture of the Residential Recording Studio

2015· dissertation· en· W3121182847 on OpenAlexaff
Gabrielle Kielich

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsCarleton University
Fundersnot available
KeywordsStudioRecreationContext (archaeology)Visual artsDesign studioWork (physics)Architectural engineeringEngineeringArtHistoryArchaeologyPolitical science

Abstract

fetched live from OpenAlex

Residential recording studios began to emerge in the 1960s.They were located in remote areas, featured onsite living accommodations, state-of-the-art technology, and recreational amenities.The "resort" studio conceptualizes the function and use of these studios as alternative workspaces.In this thesis, three case studies illuminate their development, ownership, and operation, and their intersection of work and living spaces in isolation.Resort studios were marked by diversity, but featured consistent design and working conditions.By combining the workplace and living space in relaxed atmospheres, resort studios blurred the distinction between work and leisure.However, their isolation from distractions created a concentrated creative work environment.The resort studio highlights music production as a social process beyond an industrial context, and draws attention to the confusion around musicians' work as play.This thesis situates the resort studio within the continuum of studio configurations to contribute to a more complete version of studio history.the seeds of inspiration for this project in 2009 by so enthusiastically telling me about those dart games at Rockfield Studios.My colleagues in the Music and Culture program have made the past two years thought-provoking, with particular thanks to Niel Scobie for relatability in the maturestudent experience.From Carleton to Oslo, I would like to recognize and extend a very big thank you to Kyle Devine.His trusted and ongoing advice, insight, and inspiration make my academic experience richer and my approach to it more informed.In addition, I am grateful to Kyle, along with John Shepherd, for giving me valuable opportunities for professional development.Thanks to Matthew for being my best friend and for understanding what friendship means.Thanks also to Lisa M

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)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.006
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.043
GPT teacher head0.317
Teacher spread0.274 · 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
GenreOther

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

Citations0
Published2015
Admission routes1
Has abstractyes

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