Bibliographic record
Abstract
With more than 10,000 conditions connected to pathological genes, genetic science has the potential to impact illness experience substantively. Building on medical sociological and science studies literatures, my study analyzes how genetic discourses and technologies shape the lived-experience of Huntington Disease (HD). Analysis draws on in-depth interviews conducted in Canada with 24 individuals with the HD mutation and 14 caregivers (e.g., spouses). Study findings detail how genetic discourses and illness experiences intersect to produce “genetic suffering,” a participant-derived concept describing a novel modality of suffering. Genetic suffering is detailed in relation to four themes: 1) Guilt, responsibility, and genetic inheritance, 2) Chance, uncertainty and genetic testing, 3) Ambiguity and genetic onset, and 4) Fatalism and genetic prognosis. After describing the intersections between the science of genetics and suffering in HD families, I discuss the implications of study findings for debates on genetic responsibility and consider the unintended consequences of genetic technologies.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.050 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".