Dancing Through The Decades: Essays & Screendances
Bibliographic record
Abstract
ABSTRACT Reshaping personal narratives is important for older professional dance artists to maintain longevity in an aging body. Inevitable physical constraints, economic and career challenges, and family responsibilities, create obstacles in the body/mind. How do dancers pivot those intervals of crisis into reinvention? Making opportunities to synthesize one’s life work and situated knowledge for personal nourishment and professional validation, can also contribute to the field of dance. One approach is to turn a lifetime of embodied dance knowledge into a written document to grow the dancer’s voice in the scholarly dialogue. A rare artist who continues to inspire is French Canadian dancer/choreographer Louise Lecavalier, who, well into a successful performing career, offered her first major choreographed work, So Blue at age 57. Sparked by seeing So Blue at New York Live Arts in 2015, and entering graduate school at the University of Hawai‘i Mānoa in 2017, I began to look at issues related to age(ing) dancers, which culminated in a series of five personal reflection essays that track my Practice-as-Research (PaR) project. I began making screendance self-portraits that employed newly learned digital dance technologies. Then came the COVID-19 pandemic lockdown and I turned toward self-reflexive research, as corroborated in the writings of Kim Etherington and Lynette Hunter. Lecavalier’s So Blue inspired me to research my past dance works, previously buried with the stain of tragedy and betrayal. I designed a PaR archiving process to de-traumatize my personal and political narrative in dance to recast my memories and stories. I digitized, viewed, and responded to old works and then shared them with dance colleagues and conducted follow-up interviews with Patricia Chen, Seán Curran, Janet Lilly and Lisa Sokolov. I have discovered that the practices of making self-portrait screendances, and of engaging with my own ‘de-traumatizing the archive’ process, has proven to be an effective method to dissolve embodied negative psychological imprints thereby enabling me to bring my dances into the light of day. My personal experiential knowledge as a dance artist, who came to academia later in life, can be a valid contribution to the collective insight, by creating a revived constellation of dance connection to add to the bricolage of stories.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".