Bibliographic record
Abstract
Most scholarly studies of dance in some way relate to personal experiences in dance performance.My own participation in dance has perhaps been less intensive than others', involving a few years of training during childhood.However, these experiences left a deep and lasting impression, coming to fruition in my fi rst exercise in the interpretation of ancient Maya dance in the form of my ma thesis (Looper 1991a).This thesis was partly inspired by a collaborative experiment with Kathryn Reese-Taylor (then a graduate student at the University of Texas at Austin, now a professor at the University of Calgary) and Yacov Sharir of the Department of Theatre and Dance at the University of Texas at Austin.Although we never published our results, our explorations combining computer animation and body movement to decipher images of dance on Maya ceramics made me aware of the diverse methods available to explore dance in an archaeological context.It was also Kathryn who convinced me of the importance of studying contemporary Mesoamerican performance as a clue to understanding dance in an archaeological time frame.A few years later, while living in Guatemala from 1993 to 1997, I had the opportunity to see a variety of Maya festival dances and to participate in social dancing.Focused interviews with dance offi cials and participants began only after I had conceived the idea for this book and were conducted between 2001 and 2007.This fi eldwork took place in several locations, including Chamula and Zinacantán in Mexico, and Chichicastenango and Chajul in Guatemala, but mainly in San Andrés Sajcabajá and San Andrés Xecul in Guatemala.Full results of this fi eldwork
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.365 | 0.208 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".