Between tradition and innovation in new genetics: The continuity of medical pedigrees and the development of combination work in the case of Huntington's disease
Bibliographic record
Abstract
An extensive discussion is currently under way regarding the novelty of the 'new genetics'. Although a linear model of innovation shows the new genetics as a technology transfer from biology to medicine, an intersection model suggests the new genetics as a bi-dialectical exchange between biology and medicine. The purpose of this paper is to articulate the role of clinical tools from the viewpoint of an intersection model. This paper traces how medical pedigrees (i.e. visual tools used to transcribe family information) have been standardized by genetic counsellors in clinical settings, and subsequently combined with other biomedical inscriptions. The findings demonstrate that development of new genetics was facilitated not only by the introduction of molecular technologies, but also by their use in combination with medical pedigrees. In this manner, clinical practitioners, laboratory workers and lay support group members have collaborated in order to mobilize genealogical information and organic materials as biomedical properties.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".