Exploring Ethnomathematics in Traditional Dance Movements: A Study of the Sigeh Penguten Dance of Lampung, Indonesia
Bibliographic record
Abstract
Purpose of the study: This study aims to determine ethnomathematics activities and mathematical concepts applied in various Sigeh Pengeten dance movements. Methodology: The type of research used in this study is qualitative research with an ethnographic approach. Data were obtained through interviews, observation, and documentation. The research instruments consisted of the primary instrument, the researcher herself, and supporting instruments in the form of interview guidelines, observation sheets, and documentation tools. Method and source triangulation were used to validate the data. Main Findings: The results of this study indicate that in each sigeh penguten dance movement, counting activities are implemented by adjusting the fast or slow music beat in the form of a repetition of 1 x 8 counts. Some sigeh penguten dance movements implement measuring activities when the movement moves to adjust to the next floor pattern change. The concept of one-dimensional geometry is depicted from the movement that forms a straight line floor pattern. The concept of two-dimensional geometry is depicted from the shape of the floor pattern in the form of triangles, rectangles, squares, trapezoids, and circles. Reflection and rotation geometric transformations. The conclusion of this study is that in the sigeh penguten dance movements there are mathematical activities and mathematical concepts. Novelty/Originality of this study: This research can be used as input for educators to make ethnomathematics an alternative in the mathematics learning process, so that it can help improve learning outcomes and students' interest in mathematics learning.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".