Management for publicizing Thai dance in a foreign country : the case study of lor (love, obsession, revenge) performed at Fei & Milton Wong Experimental Theatre, Canada
Bibliographic record
Abstract
The objective of this research was to study and to shape concepts in the management for publicizing Thai dance in a foreign country. This study employed Love, Obsession, Revenge, or shortly, LOR as a case study and this Thai dance had already been performed at the Fei & Milton Wong Experimental Theatre, Simon Fraser University, Canada. Autoethnography methodology was applied as the research method in this study as the researcher was part of the team. Nineteen observations were conducted and analyzed by comparing and contrasting theories and personal experiences. The findings revealed that partnership with international organization facilitated management for publicizing Thai dance in a foreign country in many angles. However, mutual interest and abilities were basis for initiating a collaboration. International regulations had an influence on preparation and performing Thai dance. There were three options of marketing plan that the company must decide; Cultural Exchange, Self-Marketing, Marketing by Other. Managing to publicize Thai dance abroad consisted of processes in production and management. Also, the research found that there were options for Thai dance companies whose budget were deficit. Self-funding, sponsorship and joining international festivals with financial supports were alternatives. Finally, Thai dance was a bridge between cultural diversities despite adaptations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".