“Dance Your Ph.D.” in VideoConfeDance: Developing a Blended-Method Dance Workshop for the Popularization of Science Through Choreography
Bibliographic record
Abstract
This teaching practice report concerns a doctoral workshop developed by the authors in order to prepare Ph.D. students to participate in "Dance Your Ph.D." -an international contest of online videos, whereby doctoral students use dance to communicate their research.This workshop provides Ph.D. students with the theoretical and methodological basis, as well as choreographic tools, and the self-confidence necessary to take part in the contest.The first edition was organized fully online due to the COVID-19 lockdown.This initial constraint led to the development of a series of techniques that enabled holding a dance workshop remotely, using the Teams software.In this report, we describe how we adapted to organize the workshop online and how this led to pedagogical innovations that we continued to use in subsequent hybrid iterations of the workshop.Discussing the possibilities and challenges presented by our pedagogical approach, we position this text within related literature debates and identify directions for future research for both embodied and virtual pedagogies.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".