Blending cultures: the intersection of A.I., VR, and intercultural identity in techno-choreography
Bibliographic record
Abstract
This practice-based research aims to investigate the potential of using artificial intelligence (A.I.) and virtual reality (VR) to enhance choreographic practice and explore the concept of ‘hybrid cultural identity’ through professionally trained dancing bodies, by incorporating cultural objects such as chopsticks, fans, gaoqiao and silks. The hypotheses are that cultural objects as instruments could influence the generation of movement consequences and that VR and ChatGPT could help choreographers generate new movement vocabularies, patterns and structures in the process of techno-choreography. \n \nAs an intercultural artist with touring and working experiences in China, the United Kingdom, the United States of America, Canada, Belgium, and Malaysia, I argue for ‘identity as a dynamic concept’, as Cantle points out in the study of interculturalism. It should not be solely labelled by race or skin colour, especially for choreographers and dancers (2020: n.p.). Artists' identities are formed according to the hybridity of their inner world, which is built up by training, dancing experience, learning different cultures and techniques, and the outer world, which is the environments they engage with. \n \nThe use of VR and ChatGPT, along with the incorporation of cultural objects, enables me to create immersive and interactive environments that can inspire new choreographic ideas. Additionally, ChatGPT is utilised as a tool to generate new movement patterns and narrative structures that can be incorporated into the choreography. The outcome of the research is the generation of interactive performance frameworks that enable embodiments of cultural objects and dancing bodies in digital performance. This study contributes not only to aspects of choreography and dance research in the contemporary global and intercultural contexts in which I have worked but also to cultural studies and computer science studies that engage body-worn technologies or develop human-computer interfaces.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.010 |
| 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".