Noise(s) and sound(s): comparing various conceptualizations of acoustic phenomena across languages
Bibliographic record
Abstract
Musical listening focuses on perceptual attributes of the sound itself (e.g. pitch, loudness), whereas everyday listening focuses on events to gather relevant information about what happens in our environment (e.g. car approaching), that is, not about the sound itself but rather about noise as produced by sources and actions, and the effect of sound/noise on listeners. Previous linguistic analyses of discourses identified different conceptualizations of everyday/environmental noises and of musical sounds, as well as different conceptualizations for acousticians and non-acousticians. A more extensive psycholinguistic investigation was conducted to evaluate how different languages (not only consensual vocabularies) differently structure the semantic space of acoustic phenomenon. An open questionnaire was administered to expert listeners (acousticians) and naïve listeners from 5 different languages (French, Italian, Spanish, German and English) to collect linguistic resources available in each language and used in discourse to describe acoustic phenomena in their native languages. Presented in this paper are the first results of the comparative psycholinguistic analysis, in terms of linguistic devices and correlated conceptualizations, focusing on - the distinction between noise(s) and sound(s) across languages - the difference between scientific discourses and common sense discourses within and across languages.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".