Science and Suffering: Genetics and the Lived Experience of Illness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it