Is style reproduction a computational creativity task?
Bibliographic record
Abstract
Is style reproduction a valid computational creativity task? Does producing output 'in the style of' an existing creator contribute to computational creativity research? Where is the creativity in imitation or replication of an existing style, and where does style reproduction fall into what has been criticised as `pastiche' rather than credible creative activity? This paper tackles these debates, which have been under-addressed in computational creativity literature. We review the presentation of past work in style reproduction, and consider the fit of such work into evolving definitions of computational creativity research. As part of this, we consider style reproduction itself as a creative task, both within and outside computational forms. We discuss various points of interest that emerge in the analysis, such as control in the creative process, intentionality and effort. Our work gives a more objective understanding of the level of creativity present in style generation, and specifically what value it brings to computational creativity research.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".