Languages and conceptualization of soundscapes: A cross-linguistic analysis
Bibliographic record
Abstract
In the past decade, soundscape research has emerged accounting for acoustic phenomena as they are perceived and assessed by humans. In this view, concepts and methodologies from social sciences and humanities are needed to identify the diversity of conceptualizations across time, space, and languages. Specifically, our approach relies on linguistics and psychology in analyzing how people describe their sensory experience (what is being said and how it is being said), in order to identify different conceptualizations conveyed in their discourse. We first investigate the linguistic resources available in different languages with a cross-linguistic survey of free-format verbal descriptions of acoustic phenomena in European languages (e.g., French, English, Dutch, Spanish), extending the pilot investigation by Dubois and Guastavino (2008). Then, coupling this linguistic analysis with cognitive theories on categorization, we can infer a diversity of conceptualizations for the same acoustic phenomena. This approach further allows us to overcome some limitations of current survey design: the use of closed-ended questions confining responses to categories pre-defined by the experimenter, and basic translations not taking into consideration semantic languages specificities. Our results provide a theoretical grounding and methodological guidelines for designing questionnaires for cross-cultural evaluation of soundscapes.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".