Dance Your PhD: Embodied Animations, Body Experiments, and the Affective Entanglements of Life Science Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it