The Application of Q-Methodology to the Study of Criteria Used by Adolescents in the Evaluation of Their Musical Compositions
Bibliographic record
Abstract
This study employs Q-methodology to investigate the criteria adolescents use when evaluating their musical compositions. Thirty-two adolescents (aged 13–14 years) balanced for gender and prior experience of formal instrumental music tuition (FIMT) participated in a Q-sort procedure based on forty-six items. The items were formulated from four sources: specialist music teacher interviews, adolescent focus group discussions, music curriculum documents, and academic papers investigating the assessment of music composition. The resulting data was analysed using factor analysis. In Q-methodology, these factors represent groups of adolescents based on the criteria they considered important for evaluating their musical compositions. Three main groups of adolescents were associated with the majority of participants. The criteria found to be important to each group were interpreted as: (1) composing an appealing piece to a preconceived formula, (2) composing a novel, abstract and interesting piece, and (3) composing an inventive and imaginative piece to a preconceived formula. Comparisons between the criteria used by adolescents and the criteria regarded as important by music teachers are also examined, as well as differences between the adolescents' criteria based on their prior experience of FIMT. Suggestions for future research and the implications of the findings for music education 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 | 0.136 | 0.269 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".