Intercultural Musical Creation Drawing on the Theory and Practice of Turkish Models
Bibliographic record
Abstract
This study creates a new collaborative space among diverse cultures, driven by Turkish music models, and using aspects of the related rule-based improvisatory genre and process known as taksim. My work draws on historical sources to introduce the theory and practice of Turkish makam music, while using a specific methodology to enable musical creation. By doing so, I present new knowledge about collaborative music-making and performance. This work benefits from the “research-creation” approach, which removes the traditional separation between the study of music (musicology, music education) and the practices of creating music (composition, performance). By combining educational and creative methodologies with research techniques, my project presents multidisciplinary content involving music education, performance, and folklore. The project allows musicians to exchange musical ideas, acquire new techniques, compose musical parts, and expand their musical vocabulary. I analyze the musical and creative outcomes of the data created by two research-based music ensembles. Both the Sofra Ensemble and Musiki Flow were composed of Turkish and North American musicians — the first in Calgary/AB and the latter in Newnan/GA, respectively. The collaborative musicians’ musical backgrounds were combined with new information about Turkish music and its practical methods The study had a significant cultural impact on certain North American communities, allowing musicians and audiences from different cultures to come together through public concerts. By choosing to perform contemporary arrangements of traditional musical pieces, some of which dealt with sensitive social issues, my research exhibits a strong commitment to human rights. Through responsible and thoughtful advocacy my project supported children's rights, women’s empowerment, equal opportunity, and respect for diverse communities.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".