A Study of Isan Folk Music Creation Using a Sampler for Music Education
Bibliographic record
Abstract
This research represents a creative exploration in the field of music (Practice as Research: PaR) with the objectives of 1) studying the use of a sampler from Isan instruments and instruments from various cultures to create musical compositions using music software, and 2) examining the appropriate contexts for using a sampler in creative musical works. The research findings reveal that 1) Isan folk music possesses unique characteristics in terms of sound quality and performance techniques, while other musical elements can be combined with it in a technologically driven creative process. This involves connecting the concepts and methods of both traditional and diverse cultural music to create unified compositions using technology, such as recording musical notes in standard notation, exporting them as MIDI files, and then importing them into audio recording software to refine the sounds for complete songs. 2) Regarding the appropriate contexts for using a sampler in creative music, three main contexts were identified: an Isan folk music composition context, a music education context, and a commercial or music business context. In summary, the process of using a sampler for creative music works can bridge the concepts of various cultural music and integrate them with technology. Furthermore, this process can be extended to enhance interdisciplinary learning and teaching methods, making it applicable in diverse contexts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".