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Record W2062892999 · doi:10.1353/tech.2014.0054

Orchestrating Automobile Technology: Comfort, Mobility Culture, and the Construction of the “Family Touring Car,” 1917–1940

2014· article· en· W2062892999 on OpenAlexfundno aff
Gijs Mom

Bibliographic record

VenueTechnology and Culture · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
FundersUniversity of WashingtonUniversity of TorontoJohns Hopkins UniversityYale UniversitySmithsonian Institution
KeywordsOrchestrationAutomotive industrySightEngineeringAeronauticsArchitectural engineeringVisual artsArtAerospace engineeringMusical

Abstract

fetched live from OpenAlex

During the social and technical construction of the affordable “family touring car” in both the United States and Europe, one of the urgent projects was the abatement of noise. As a part of the emergence of “automotive comfort,” noise abatement took place during one of the costliest operations in the history of automotive technology: the closing of the body. From an open tourer, the car, during the interwar period, developed into a sedan, encapsulating the driver and his passengers and drastically altering their sensorial intake, especially sight and sound. Thus, car engineering became an engineering of the senses. In this article focusing on the American car culture, it is argued that sound “orchestration” was necessary to enable the automotive subject (the nuclear family) to concentrate on what it liked most: gazing outside the car body. The tourist gaze was rescued through the orchestration (both domesticating and fine-tuning) of the car as a sound machine.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.017
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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.

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

Citations7
Published2014
Admission routes1
Has abstractyes

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