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Record W4388941352 · doi:10.1080/17508061.2023.2280435

The birth of noise in modern China: Radio, amateur engineering, and the sonic network

2023· article· en· W4388941352 on OpenAlexafffund

Bibliographic record

VenueJournal of Chinese Cinemas · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Medicine
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAmateurChinaTelecommunicationsNoise (video)EngineeringAcousticsHistoryComputer sciencePhysicsArchaeology

Abstract

fetched live from OpenAlex

This article argues that noise was born in modern China with the advent of radio and radio broadcasting. The media specificity of radio required that any radio listener must be an acoustic engineer to some extent, dealing with “noise current,” “tunable hum,” and “interference” before tuning into the desired radio program. This novel experience of listening as engineering gave rise to a new conceptualization of noise not merely as unpleasant sounds (the music-noise dichotomy), but also in terms of modern information technology (the signal-noise dichotomy). Integrating both modes of understanding noise, this article further suggests that the affordable radio sets and the public-oriented radio broadcasting together constituted an unprecedented sonic network, one that transmitted heterogenous sonic information from all over the world back home and at the same time deprived of any individual’s agency in deciding what to listen to. Living in this sonic network, one’s body-mind had to function as if it were a radio set, exposed to overwhelming information streams and trying to filter out noise as both disturbing signals and undesired contents. Radio noise is therefore symptomatic of the most fundamental dilemma China was facing in an era of global modernity, national crisis, and information explosion.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.548
Threshold uncertainty score0.151

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.209
Teacher spread0.200 · 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.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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