Floating Departures: Developing Quarantine Dance Technique as an Artistic Practice Beyond the Pandemic
Bibliographic record
Abstract
We describe our process of quarantine dance technique in making the dance film and meditation, Floating Departures. This work, created during lockdown in 2021, brings together dance movement, poetry, painterly styles, and sound to explore cyclical patterns and points of departure in movement and life. To create Floating Departures we used a broad range of technologies–from everyday objects to smartphones to AI art systems. We experiment with various techniques to record ourselves and bring our movement together in a shared digital space with post-production video editing techniques. Using a bricolage approach, we incorporate materials, such as bubble wrap and balloons, to transform our spaces and explore our personal experiences during lockdown. We construct multiple layers of reality that are further transformed in unanticipated directions with AI technologies. Through our creation process, we develop a collective physical body to explore a new realm, unbound by reason or logic, that was only made possible through our remote collaborative processes and technologically-mediated interactions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".