Situational and person-related factors influencing momentary and retrospective soundscape evaluations in day-to-day life
Bibliographic record
Abstract
Soundscape research draws on both experiments conducted in laboratory settings and studies in the field to explore peoples' perception and understanding of their acoustic environments. One opportunity to combine the strength of both approaches is the so-called Experience Sampling Method (ESM). This method was used to investigate the influence of situational and person-related variables on soundscape evaluations. Further, the relationship between momentary and retrospective soundscape judgments was explored. In the course of the 7-day ESM study, 32 participants were prompted ten times per day by a smartphone application to evaluate their soundscape and report on situational factors. Additionally, they performed summary retrospective judgments evaluating the whole of each day and their whole week. Upon completion, an exit interview probed personality traits (e.g., Big Five, information processing styles). Results revealed that both situational and person-related factors significantly contributed to the judgments of three soundscape dimensions (pleasantness, eventfulness, familiarity). Retrospective judgments of soundscape pleasantness were not only the average of the momentary judgments, but were also affected by the peak moment, the linear trend of the experience, and a person's mood while performing the judgment. Hence, the study provides valuable insights into the complex structure of momentary and retrospective soundscape evaluations.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".