Possible selves as a source of motivation for musicians
Bibliographic record
Abstract
Music can be a core element of the sense of self. Integration of the future, possible musical self within the self-concept helps to account for the enormous investment of time and energy necessary to become a musician. In this qualitative study, we explore the motivational dimensions of the possible musical self. Possible selves exist in multiple domains with both positive and negative elements. Respondents from a diverse, snowball sample ( N = 204) of musicians completed an online survey describing their hoped for, expected and feared musical selves. Coding of the responses identified major themes. The ‘hoped for’ selves yielded four main themes among 171 responses: improvement, social connection, success and enjoyment. The ‘feared’ selves yielded a total of five main themes among 160 responses: being a poor musician, injury/illness, financial difficulty, lack of knowledge and lack of social connection/recognition. The ‘expected’ selves yielded only one additional category, negative expectations. The balance or tension between the positive and negative elements of possible selves is analysed to produce a composite description of the possible musical self. Limitations of the study and links between the present results and possible selves theory are discussed.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".