Dance Your PhD: Embodied Animations, Body Experiments, and the Affective Entanglements of Life Science Research
Bibliographic record
Abstract
In 2008 Science Magazine and the American Academy for the Advancement of Science hosted the first ever Dance Your PhD Contest in Vienna, Austria. Calls for submission to the second, third, and fourth annual Dance Your PhD contests followed suit, attracting hundreds of entries and featuring scientists based in the US, Canada, Australia, Europe and the UK. These contests have drawn significant media attention. While much of the commentary has focused on the novelty of dancing scientists and the function of dance as an effective distraction for overworked researchers, this article takes seriously the relationship between movement and scientific inquiry and draws on ethnographic research among structural biologists to examine the ways that practitioners use their bodies to animate biological phenomena. It documents how practitioners transform their bodies into animating media and how they conduct body experiments to test their hypotheses. This ‘body-work’ helps them to figure out how molecules move and interact, and simultaneously offers a medium through which they can communicate the nuanced details of their findings among students and colleagues. This article explores the affective and kinaesthetic dexterities scientists acquire through their training, and it takes a close look at how this body-work is tacitly enabled and constrained through particular pedagogical techniques and differential relations of gender and power. This article argues that the Dance Your PhD contests, as well as other performative modalities, can expand and extend what it is possible for scientific researchers to see, say, imagine and feel.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".