Life Writing in the Era of Genetics: Contemporary Genetic Risk Narratives in Great Britain and America
Bibliographic record
Abstract
The development of genetic science brings forth a third group besides the healthy and the ill: the high-risk group who carries certain disease-related genes. In the era of genetics, people try to assess risks with statistical numbers and eliminate risks by Western medical measures. In this context, personal genetic risk narratives (usually in the form of memoirs) emerged in Great Britain and America in the 1990s. The thesis has a close reading of three British and American genetic risk memoirs and wants to find the characteristics and values of the new genre. The memoirs are featured by their vivid description of the narrator’s difficult and complex situation in face of genetic risks. In an era when the body is dominated by statistical numbers, these narratives make personal meaning of impersonal statistics. Genetic risk narratives express a strong belief in genetic technology and Western medical myth. However, the narrative divergence and self-contradiction in the memoirs exposes the limitation of genetic determinism and thus deconstructs the Western medical myth.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.036 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".