Pedagogy and Performativity: Rendering Laboratory Lives in the Documentary<i>Naturally Obsessed: The Making of a Scientist</i>
Bibliographic record
Abstract
A recently released documentary on life in a protein crystallography laboratory offers an exemplary opportunity to examine how a popular account of scientific training models narrowly defined norms of masculinity and mentorship and simultaneously sets these as the tacit conditions for success in science. Rather than treating this documentary as a good or bad representation of what life in the lab is actually like, this analysis draws attention to how the scientists featured in the film perform for the camera and how the filmmakers splice together the action to animate an engaging story. This essay shows how this popular and widely circulating documentary frames science as a game to be won and stages scientific success on an agonistic playing field. Those who can "make it" are those who are tough enough and those who are willing and able to get entangled in the taunting, jesting, and jostling relationships that appear to be required for mentorship in this lab. The essay argues that this documentary tethers this model of success in science to restrictive norms of masculinity and in so doing promotes a pedagogical culture that fosters competition, rivalry, and ritualized shame. Feminist theories of performativity are engaged to consider the iterative processes through which narrowly circumscribed masculinities and styles of pedagogy are sedimented and naturalized. This essay aims to spur renewed attention to the care historians and anthropologists might take to examine the often hidden tropes that are lurking inside the stories about science that we find so salient.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.070 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".