The performing and teaching practices of Iranian traditional musicians in Canada
Bibliographic record
Abstract
AbstractThis study investigates change and continuity in the performing and teaching practices of musicians who have emigrated from Iran to Canada. Iranian communities in Canada are growing at a fast pace due to a variety of socio-political and economic challenges in Iran and the prospects of a better life in Canada. Therefore, the main focus of this research is on the ways in which immigration impacts the practitioners of Iranian Traditional music in this multicultural society.Through employing a qualitative method, including ethnographic fieldwork and semi-structured interviews, this research examines how Iranian musicians living in Montreal and Toronto are adapting their performing and teaching practices to their new environment. Analysis of the gathered data has focused on a range of pre-determined, as well as emerging themes including the participants' musical background, their performing experiences in Iran and Canada (concert repertoire, audience, concert logistics), their teaching experiences in both locations (students and teaching methods), the musicians' perceptions of and attitudes towards life in Canada, the perceived advantages and disadvantages of this environment, and their adaptation/reception into society.In spite of the growing presence of the Iranian population in Canadian urban centers, the visibility of the Iranian culture is still an issue and Iranian traditional music is struggling in finding its place on the musical map of the world. Therefore this research is a contribution to a growing international body of research on Iranian music, a subject that has received limited academic attention. The author also argues that a better understanding of how immigrant musicians are adapting their cultural practices to the multicultural Canadian environment can assist governments in the development of their community policies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".