A cognition-analogous approach to early-stage creative ideation support in music composition software
Bibliographic record
Abstract
Examination of the underlying principles of creativity reveal theoretical aspects that have not been well explored in creativity facilitation software.Most significantly of these, there has been little investigation into exploiting the distinctions between early-and late-stage creative processes and the attendant differences in cognitive processing active at those times, nor into employing the structural scaffolding embedded within creative works and the manner in which these can be extracted and harnessed to define levels of abstraction through which the material can be viewed and manipulated.The Wheelsong project was conceived to exploit these principles, in the service of devising more creatively facilitative music composition tools, by focusing on these earlier, exploratory stages of the creative process, and by privileging structure over minutiae, in alignment with the mode of cognition (and corresponding user needs) that dominate the exploratory phase.Explorations conducted with Wheelsong demonstrate that the platform embraces broad stylistic and cultural ranges of output.Experiments comparing the creative merits of early-stage, fragmentary outputs produced by Wheelsong against those produced by traditional representation schemes show a substantial improvement in both subjective quality and diversity indicators adhering to the structurally produced candidates, as measured by human judges.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".