Mon Dance: Creating Standards to Continue the Performing Arts of Thai-Raman
Bibliographic record
Abstract
This investigation, Mon dance: Creating standards to continue the performing arts of Thai-Raman, has the following objectives: 1) To understand the history, current conditions of and problems with Mon dance of the Thai-Raman; 2) To examine the standards of Mon dance; 3) To create standards to continue the performing arts of the Thai-Raman. This qualitative research was conducted in three central Thai provinces: Pathum Thani, Nonthaburi and Samut Prakan. Data was collected from documentary study and field data by means of observation, interview and group discussion. Workshops were also held with a total of 100 attendees, comprised of 20 key informants, 50 casual informants and 30 general informants. Data was validated using a triangulation technique and findings are presented using a descriptive analysis. The research highlighted current conditions and problems in dancing process, melody, costume, performers, time and rituals,which have similarities and contrasts depending on the environment and the social and cultural changes in each area. The standardization of the dance was found to be similar to the dancing process, melody, costume, performers, time and rituals. Traditions are not supposed to change in order to demonstrate the identity of Mon Dance. It was concluded that the dance process has 6 indicators and points were set in the performance judging criteria as follows: 170 points for the melody with 3 indicators, 195 points for the costume with one indicator, 30 points for the performers with six indicators, 80 points for the time with 2 indicators, 25 points for the rituals with 3 indicators and 40 points for meeting the assessment criteria. The passing score for the dance will be 80%.
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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 | 0.006 | 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 | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".