Genetic Determinism and Discrimination: A Call to Re-Orient Prevailing Human Rights Discourse to Better Comport with the Public Implications of Individual Genetic Testing
Bibliographic record
Abstract
Genetic testing can not only provide information about diseases but also their prevalence in ethnic, gender, or other vulnerable populations. While offering the promise of significant therapeutic benefits and serving to highlight our commonality, genetic information also raises a number of sensitive human rights issues touching on identity and the perception thereof, as well as the possibility of discrimination and social stigma. It stands to reason that the results of individual screenings could haplessly be used to make general assumptions about entire ethnic or gender groups. In this manner, genetic information can directly influence identity by impacting and perhaps even reframing conceptions of group rights and dimensions of self-identification, thus importing constitutional scrutiny on questions of dignity and discrimination in particular. Is there a risk of collective stigmatization deriving from discrete testing of self-identified individuals? Would such stigmatization impinge on individual dignity by the exogenous imposition of ethnic or gender/sexual identity? If so, what norms can most adequately respond if and when individual and group interests diverge? These questions are examined from a comparative perspective.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".