Defining the Scope of Public Engagement: Examining the “Right Not to Know” in Public Health Genomics
Bibliographic record
Abstract
While the realm of bioethics has traditionally focused on the rights of the individual and held autonomy as a defining principle, public health ethics has at its core a commitment to the promotion of the common good. While these two domains may at times conflict, concepts arising in one may also be informative for concepts arising in the other. One example of this is the concept of a “right not to know.” Recent debate suggests that just as there is a “right to know” information about one's genetic status, there is a parallel “right not to know” when it comes to genetic information that if communicated, could be detrimental to an individual's social or psychological well-being. As new genetic technologies continue to change the nature of genetic testing and screening, it is crucial that normative frameworks to guide and assess genetic public health initiatives be developed. In this context, the question of whether a “right not to know” may also be said to exist for populations on a public health level merits attention.
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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 | 0.085 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.018 | 0.134 |
| Scholarly communication | 0.025 | 0.038 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".