French Families, Paper Facts: Genetics, Writing, and Intimate Histories.
Bibliographic record
Abstract
Biogenetics dominates contemporary discussions—scientific, media, and in everyday talk—about the origins of diseases, ethnicities, tribes, and nations. This dissertation sheds light on the dynamics—of affect, history, memory, and bureaucracies—that shape and animate biogenetic explanations. The dissertation builds on fine-grained fieldwork at genomic databases, genealogical registries, family history societies, and medical clinics in Quebec to illuminate the conceptions of family, heredity, and human difference—often intertwined—that are defining analytic boundaries and the acceptance and use of evidence within medical genetics. The dissertation focuses on the multiple trajectories of a particular form of medical genetic evidence, the Catholic Church vital record. The Catholic Church mandated personal data record-taking during the Counter Reformation and, as a result, historically Catholic European countries and their numerous African, Asian, and American colonies have some of the most comprehensive catalogues of historic birth and marriage information in the world. Geneticists and medical researchers in Quebec use these records to infer long durée family genealogies and then deduce the origins of “French diseases.” The dissertation investigates the clinical, experimental, and historiographic rationales that sustain their genealogical conclusions and etiological explanations, as well as the rationales sustaining explanations of those who oppose them. The dissertation unearths the exigencies—from colonial French Church writing strictures to laboratory infrastructures—of how medical workers, genealogists, and people beyond the purview of health, medicine, and genealogy delineate families, risk, and race. In looking at how written cultures, colonial histories, family practices, and feelings about the past play a role in the production of genetic knowledge, the dissertation broadens the scope of traditional scholarly investigations of race and medicine. Many of these investigations, working within a Foucauldian rubric, have focused narrowly on constraints placed on consciousness by domineering state and social power structures. In contrast, this dissertation illustrates how diverse investments in ancestors and pragmatic choices about evidence also shape the styles of reasoning about family, heredity, and human groups that animate biogenetic worlds.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".