Applying consistent fuzzy preference relations to measure user perceived service quality of information presenting web portals
Bibliographic record
Abstract
Listening to music aids regulation of emotional arousal and valence (positive vs. negative). Anxiety sensitivity (AS; fear of arousal-related sensations) increases the risk for emotion dysregulation and associated coping behaviors such as substance use and exercise avoidance. The relationship between AS and music listening, however, has received very little attention. This study (1) used exploratory factor analysis of 53 items drawn from three previously validated measures of reasons for music listening to identify the core reasons for listening to music among university students and (2) explored associations between AS and reasons for music listening. Undergraduates (N = 788; 77.7% women; Mage = 19.20, SDage = 2.46) completed the Anxiety Sensitivity Index-3, Motives for Listening to Music Questionnaire, Barcelona Musical Reward Questionnaire, and Brief Music in Mood Regulation Scale. Six core reasons for music listening were identified: Coping, Conformity, Revitalization, Social Enhancement, Connection, and Sensory-Motor. Over and above age and gender, AS was associated with Coping and Conformity-reasons that involve relief from aversive emotions. AS also was associated with listening for Connection reasons. AS was not associated with Revitalization, Social Enhancement, or Sensory-Motor-reasons that involve rewards such as heightened positive emotions. Results suggest that individual differences may influence why people incorporate music listening into their day-to-day lives. Further longitudinal and experimental research is needed to establish directionality and causality in the observed relationship of AS to relief-oriented reasons for music listening. Findings may guide music therapists' efforts to tailor treatment for individuals at risk for anxiety and related mental health problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".