Side by Side: Reflections on Two Lifetimes of Dance
Bibliographic record
Abstract
Telling stories about our experiences in dance brings to light unconscious knowledge and memories of the past and helps us understand our own decisions and practices. Reflexivity and story telling is central in the process of remembering and embodies some of the key aspects of autoethnography as a research tool. We are directed to examine and reflect on our experiences, analyzing goals and intentions, making connections between happenings and recounting each single experience. Dance has the potential for positive impact on both physical and mental health among professional dancers as well as among dance students and has the power to connect them to culture and community in unique and important ways. Research has provided evidence that arts engagement provides positive forms of social inclusion, opportunities to share arts, culture, language, and values and points to the value of the arts in the prevention and amelioration of health problems. Together with those benefits of a dance experience there is clear evidence of what can be learned in, through and about dance. In this time of the Covid-19 pandemic it seemed more relevant and poignant to examine our own experiences in dance as well as those experiences of others that have influenced our lives.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".