From ethno-mathematics to generative design: metapatterns and interactive methods for the creation of decorative art
Bibliographic record
Abstract
The research discussed in This work focuses on the development of interactive methods to image and analyze the surface designs of cultural artifacts and the generation of new designs. The project is interdisciplinary and uses methodologies from aesthetic and cultural inquiry, mathematics and computer science. The results of the research are obtained by using a combination of tools, such as neural networks, pattern recognition techniques including edge detection, and pattern generating techniques such as shape grammars. The first phase of this research focuses on the analysis of Congolese Kuba cloth and Moroccan Zillij mosaics because each has a complete and complex surface pattern with very different characteristics. Our work has three main facets: the description of the geometric content of ethno-mathematical artifacts; the classification of this content; and the generation of grammatical rules for the creation of new designs based on the studied artifacts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".