Doing dance research in pandemic times: fostering connection and support in a 7-step online collaborative interview analysis process
Bibliographic record
Abstract
Over the past two years, our global dance community has faced many challenges while coming to terms with a health crisis that drastically altered home and working lives. In this article, we focus on how dance scholars can work collaboratively during extended periods of isolation. We begin by overviewing the drastically altered environment of dance education during pandemic, then direct our focus to the ways that we, as dance scholars, were also adjusting our practices to sustain research collaborations and provide support for colleagues during extended periods of isolation and physical distancing. Expanding upon insights from prior qualitative dance research, and addressing an aspect of the research process often conducted in isolation, we describe a 7-step collaborative interview analysis process. Based on our initial trial of this process, as international dance scholars analyzing three separate dance projects, we discuss how our online analysis sessions enabled researchers (separated by space, time, and experience) to support one another, encouraging momentum and connection during a time of heightened stress and uncertainty.
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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 | 0.081 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".