The birth of noise in modern China: Radio, amateur engineering, and the sonic network
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".