Bibliographic record
Abstract
This dissertation is an ethnographic study of how notions of the human and scientific practices in a neuroscience laboratory transform under the contemporary condition of complexity. I focus on a group of researchers working in a neuroscience laboratory in Ottawa, Canada, and who have close ties with researchers in China and Taiwan. Rejecting especially deterministic and reductionist approaches, these researchers take up a notion of the human brain—the “most complex” scientific object, that is embodied, embedded in the surrounds, and emplaced in time, but also active on its own, and thus what they see as a living brain, an especially lively and active one. Captivated by the lively activity of the brain, the scientists bring dynamic, temporalized, noisy, and processual matters and methods into their scientific practices of imaging, experimenting, measuring, and modelling brains, what I term the brain complex. With the brain complex, the scientists revision the brain less as a thing, and more as a verb, a process or a doing, and captured in the term ‘activity’ that was the focus of study and materialized through dynamic interactivity. This dissertation argues that, with this revision, the scientists’ efforts to study the lively brain involve incorporating indeterminacy in their scientific practices and doing so challenges notions of science as reductive, deterministic and as a fixed practice itself, studying static scientific objects with stabilized technical apparatuses. Indeterminacy in practice instead acknowledges the change and transformation of this science, as well as the complex anthropological notions with which the brain is conjoined, namely the human, the body, and life. By following how scientific practices take up indeterminacy in this neuroscience laboratory, this dissertation challenges taken for granted understandings of the neuroscientific human as not just fixed but also dynamic, and, even further, how this dynamic human, still latent in anthropology as well, can (re)produce an uneven politics of knowledge.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.038 | 0.140 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".