Bibliographic record
Abstract
Research in the field of epigenetics challenges the assumption on which the molecular genetics of the past 50 years has been based, namely, genetic determinism. This paper reviews the social science literature that considers the social effects of the application of molecular genetics and genetic testing in connection with Mendelian conditions. It is argued that anthropologists must now go farther and respond to the challenge posed by current moves toward the implementation of genetic profiling and testing for susceptibility genes. Following a discussion of ontological problems associated with molecular genetics raised by philosophers and biologists who subscribe to epigenetics, current knowledge about molecular and population genetics of lateonset Alzheimers disease and crosscultural findings about the epidemiology of this disease are introduced. These findings illustrate the provisional nature of these bodies of knowledge and the complexity associated with susceptibility genes, which makes estimations of probabilities of individual risk unrealistic. A controlled clinical trial is discussed in which firstdegree relatives of Alzheimers disease patients are genotyped for risk for lateonset Alzheimers disease. In conclusion, the social implications of testing for susceptibility genes are discussed, with comments about the role that anthropologists might play in future research.
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.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".