The meaning of city noises: Investigating sound quality in Paris (France)
Bibliographic record
Abstract
The sound quality of Paris (France) was investigated by using field inquiries in actual environments (open questionnaires) and using recordings under laboratory conditions (free-sorting tasks). Cognitive categories of soundscapes were inferred by means of psycholinguistic analyses of verbal data and of mathematical analyses of similarity judgments. Results show that auditory judgments mainly rely on source identification. The appraisal of urban noise therefore depends on the qualitative evaluation of noise sources. The salience of human sounds in public spaces has been demonstrated, in relation to pleasantness judgments: soundscapes with human presence tend to be perceived as more pleasant than soundscapes consisting solely of mechanical sounds. Furthermore, human sounds are qualitatively processed as indicators of human outdoor activities, such as open markets, pedestrian areas, and sidewalk cafe districts that reflect city life. In contrast, mechanical noises (mainly traffic noise) are commonly described in terms of physical properties (temporal structure, intensity) of a permanent background noise that also characterizes urban areas. This connotes considering both quantitative and qualitative descriptions to account for the diversity of cognitive interpretations of urban soundscapes, since subjective evaluations depend both on the meaning attributed to noise sources and on inherent properties of the acoustic signal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".