From Flesh to Insight: More-Than-Human Affective Agency in the Scientific Process
Bibliographic record
Abstract
This paper delves into the affective more-than-human relations that shape laboratory work and production of scientific knowledge. Contrary to the prevalent anthropocentric and rationalist focus in the literature on laboratory work, this study explores the dynamic and performative interplay between human scientists and nonhuman entities, particularly tumors. The empirical setting takes places in a university hospital center and investigates an innovative precision medicine experiment that aims to derive predictions about a patient’s response to chemotherapy by testing the drug on ex vivo samples of the individual’s tumor. Drawing on posthumanist ethnographic methods, I trace the transformative trajectory of a tumor from its origin as messy piece of human flesh to its culmination into an objective and actionable prediction. Through vignettes, the paper zooms in onto affectively intense episodes within the experimental process to elucidate the more-than-human, more-than-rational mechanisms of scientific truth-claiming. These findings are examined through a new materialist lens to shed light on the performativity of affective more-than-human relations, challenging anthropocentric and rationalistic views of scientific and organizational work.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.072 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".