Assessing the ecological validity of soundscape reproduction in different laboratory settings
Bibliographic record
Abstract
The ever-growing body of soundscape research includes studies conducted both in everyday life environments and in laboratory settings. Yet, laboratory settings differ from in-situ and therefore may elicit different perceptions. The present study explores the ecological validity of soundscape reproduction in the laboratory using first-order Ambisonics and of different modes of questionnaire administration. Furthermore, it investigates the influence of the contextual factors of time of day, day of the week, and location on site on soundscape evaluations in situ and in the laboratory, based on the Swedish Soundscape Quality Protocol. We first tested measurement invariance between the computer-based and pen-and-paper administration of the soundscape questionnaire. We then investigated the influence of the above-mentioned contextual factors on soundscape evaluations, as well as the effect of stimuli selection in the laboratory. The analyses confirmed the underlying dimensions of proposed soundscape assessment questionnaires, confirmed metric invariance between computer and pen-and-paper, and revealed significant influences of time, day, and location on soundscape scales. This research represents a critical step in rigorously assessing soundscape evaluations in the laboratory and establishes solid evidence for the use of both in situ and laboratory soundscape studies.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".