Investing in Life, Investing in Difference: Nations, Populations and Genomes
Bibliographic record
Abstract
This article explores the contemporary scientific practice of human genome science in light of Michel Foucault’s articulation of the problem of population. Rather than transcending the politics of social categories and identities, human genome research mobilizes many different kinds of populations. How then might we aim to avoid overgeneralized readings of the refiguring of human difference in the life sciences and grapple with the multiple and contradictory logics of population classification? In exploring the study of human variation through the case of the ‘Quebec founder population’ at a private genome research laboratory in Canada, the article argues that the power to define and shape meanings of human variation and to organize vitality is not held by any one institution or form of scientific practice. While molecular genomics may be transforming conceptions of human difference, the laboratory is only one of many places where human genomic variation accrues value, meaning and relevance. Molecular configurations of human difference gain meaning through a traffic in populations that extends beyond the laboratory. In the case explored here this traffic in populations is constituted within a nexus of empire, national census practices and contemporary articulations of multicultural policies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".