Bibliographic record
Abstract
Research has begun to explore the nature of strong experiences of music listening, identifying a number of individual components from physiological through to psychological ( Gabrielsson & Lindström Wik, 2003 ), but this has not yet been considered in relation to mainstream theories of happiness. Drawing on positive psychology, Seligman’s (2002) framework for achieving balanced wellbeing includes the components of pleasure, engagement, and meaning. In the current study, 46 university students (median age 21) gave free reports of their strongest, most intense experiences of music listening. Accounts were analysed thematically using an idiographic approach, exploring the relevance of Seligman’s framework. Most strong experiences were positive, and occurred at live events with others. A wide range of mainly familiar music was associated with reported strong experiences, from classical through jazz and folk to old and new pop music, and experiences lasted for varying time periods from seconds to hours. Unexpected musical or non-musical events were sometimes associated with strong experiences. None of the accounts could be characterized by a single route to happiness: in addition to hedonism, engagement and meaning (particularly in terms of identity) were present in every description, and the findings thus emphasize the power of music to evoke a state of authentic happiness. The importance of taking account of the music, the listener, and the situation in order to fully understand these experiences is underlined.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".