Exploring the Validity of the Perceived Restorativeness Soundscape Scale: A Psycholinguistic Approach
Bibliographic record
Abstract
Soundscapes affect people's health and well-being and contribute to the perception of environments as restorative. This paper continues the validation process of a previously developed Perceived Restorativeness Soundscape Scale (PRSS). The study takes a novel methodological approach to explore the PRSS face and construct validity by examining the qualitative reasons for participants' numerical responses to the PRSS items. The structure and framing of items are first examined, to produce 44 items which are assessed on a seven-point Likert agreement scale, followed by a free format justification. Ten English speaking participants completed the PRSS interpretation questionnaire in two cafes in Montréal, Canada. Interpretation of participant free format responses led to six themes, which related to either the individual (personal attributes, personal outcomes), the environment (physical environment attributes, soundscape design) or an interaction of the two (behavior setting, normality, and typicality). The themes are discussed in relation to each Attention Restoration Theory (ART) component, namely Fascination, Being-Away, Compatibility, and Extent. The paper concludes by discussing the face and construct validity of the PRSS, as well as the wider methodological and theoretical implications for soundscape and attention restoration research, including the terminology importance in items measuring ART components and the value of all four components in assessing perceived restorativeness.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".