A genealogy of genealogical practices : the development and use of medical pedigrees in the case of Huntington's disease
Bibliographic record
Abstract
The purpose of this dissertation is to examine the use, role and function of medical pedigrees as part of extended networks of genetic practices. Integral to my argument is a description of geneticisation (i.e., the redefinition of family problems as genetic in origin), grounded in a set of detailed case studies of the development and use of visual tools in genetic practices. In recent years, medical sociologists have tended to link geneticisation to medicalisation (i.e., the social control by doctors over patients accompanied by the translation of social problems into medical issues). I argue that the twin notions of geneticisation and medicalisation are problematic, insofar as they embody a simplistic and negative understanding of medical activities and they prevent a sociological inquiry into the technical content of genetic practices. Medical pedigrees are visual tools used to translate family problems into visual inscriptions, in order to show the genetic nature of a given disease. The use of medical pedigrees in genetic counselling and research rests on a chain of genetic practices including the inscription of family trees, the standardisation of medical pedigrees, the combination of specialised forms of medical pedigrees with other diagnostic inscriptions, and the circulation of published pedigrees. The analysis is based on a genealogical approach, as built on a combination of historical and ethnographic methods. The genealogical approach was applied to the analysis of a long network of genetic practices centred on Huntington's disease. The analysis spans over 120 years and compares two different international settings (North America and Japan). The thesis examines how lay support group members and family members collect family narratives, family inscriptions and family trees, which were first translated by genetic counsellors into various forms of medical pedigrees, and then circulated as educational material among lay and medical practitioners. On the basis of these case studies, the conclusion is reached that the notion of geneticisation should be understood as a specific process resulting from an emerging cooperative practice between medical practitioners and lay support group members, rather than as a process of medicalisation.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".