A Knockout Experiment: Disciplinary Divides and Experimental Skill in Animal Behaviour Genetics
Bibliographic record
Abstract
In the early 1990s, a set of new techniques for manipulating mouse DNA allowed researchers to 'knock out' specific genes and observe the effects of removing them on a live mouse. In animal behaviour genetics, questions about how to deploy these techniques to study the molecular basis of behaviour became quite controversial, with a number of key methodological issues dissecting the interdisciplinary research field along disciplinary lines. This paper examines debates that took place during the 1990s between a predominately North American group of molecular biologists and animal behaviourists around how to design, conduct, and interpret behavioural knockout experiments. Drawing from and extending Harry Collins's work on how research communities negotiate what counts as a 'well-done experiment,' I argue that the positions practitioners took on questions of experimental skill reflected not only the experimental traditions they were trained in but also their differing ontological and epistemological commitments. Different assumptions about the nature of gene action, eg., were tied to different positions in the knockout mouse debates on how to implement experimental controls. I conclude by showing that examining representations of skill in the context of a community's knowledge commitments sheds light on some of the contradictory ways in which contemporary animal behaviour geneticists talk about their own laboratory work as a highly skilled endeavour that also could be mechanised, as easy to perform and yet difficult to perform well.
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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 | 0.153 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.136 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".