"What About Your Genes?" Ethical, Legal, and Policy Dimensions of Genetics in the Workplace
Bibliographic record
Abstract
Although few companies are currently applying genetic tests or using genetic data, further developments in genetics will likely increase the role of genetics in the workplace. This article discusses the complex ethical issues raised by the variety of genetic tests that could become available and proposes guidelines for dealing with genetics in the workplace. It discusses how the results of genetic testing could be used for employment purposes, and argues that the existence of unequal bargaining power in the workplace limits the validity of consent as a basis for policymaking. Instead, two specific justifications for genetic testing in the workplace are proposed: the protection of health and the avoidance of harm to others. The author suggests that genetic testing should be permitted only in exceptional circumstances and that every genetic test should be evaluated on its scientific validity and submitted to rigorous review. Existing antidiscrimination law proves to be a useful model for examining the rationality and proportionality of genetic testing in the workplace.
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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".