Ecological explorations of soundscapes: From verbal analysis to experimental settings
Bibliographic record
Abstract
Scientific studies rely on rigorous methods that must be adapted to the object of study. Besides integrating acoustic features, soundscapes as complex cognitive representations also have the properties of being global, meaningful, multimodal, and categorical. Investigating these specificities, new paradigms were developed involving linguistics and ecological psychology to complement the psychophysical approach: cognitive linguistic analyses of discourse to address semantic properties of soundscapes, and categorization tasks and distances from prototypes to investigate their cognitive organization. As a methodological consequence, experimental settings must be designed to ensure the ecological validity of the stimuli processing, (the ‘‘realism’’ evaluated from a psychological point of view, stimuli being processed as in a real-life situation). This point will be illustrated with perceptual evaluations of spatial auditory displays for soundscape reproduction. Data processing techniques should also take into consideration the intrinsic properties of the representations they account for. Examples of free-sorting tasks will be presented with measurements in terms of family resemblance of sets of properties defining categories rather than dimensional scales. New ways of coupling physical measurement and psychological evaluations will be presented in order to simulate or reproduce soundscapes in both a realistic and controlled manner for experimental purposes.
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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 | 0.023 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".