Creative blocks in musicians : an exploration of their self-reported causes
Bibliographic record
Abstract
The purpose of this study was to explore the nature and causes of blocks to musicians' creative and re-creative processes. The importance of this investigation was explained in terms of expanding existing knowledge concerning blocks. Data from 57 volunteer subjects were subjected to content analysis, modelled after Crosson (1982a & b) and Porath (1990). Six categories of causes of blocks were identified. Emergent themes included Process-Orientation, wherein blocks are described as integral elements of the creative process, as well as Problem Solving, Working Conditions, Professional Esteem, Emotion, and Physical. Quantitative analyses done on the variables duration and frequency of blocks with creative or recreative group did not support the hypotheses that associations would be found between these variables and group membership. Tentative support was found for the hypotheses that sex is related to frequency of block and also to duration. Findings confirm a hypothesized difference between the number of causes of blocks cited by musicians with varying duration of their longest block. These results have implications for counsellor awareness of, and practice in dealing with clients' blocks to creative or re-creative tasks. As well, they suggest that future research replicating the study with larger, more evenly matched, and more diverse samples is needed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".