Moral presentation of genetics-based narratives for public understanding of genetic science and its implications
Bibliographic record
Abstract
The increasing number of sequenced genes that can be used to develop tests for inherited conditions has stimulated an increasing number of genetics-based narratives by journalists, novelists, playwrights, filmmakers, and health-care educators. Genetics-based narratives are to be welcomed if the public is to understand genetic science and its implications on persons, families, and communities. However, a number of important ethical issues insist caution in their research and presentation. Just as the requirements for informed consent to undergo genetic testing exceed the requirements for informed consent to undergo other types of medical testing because of the inherent complex relationships (such as between parent and child, gene carrier and other family members, gene carrier and ethnic community) and because of concerns regarding privacy and insurance discrimination, the requirements for informed consent to present a genetics-based narrative must exceed the requirements for informed consent to present other medical narratives. We recommend that a transmedia, multidisciplinary, international conference should be convened to develop guidelines for the moral presentation of genetics-based narratives, whose deliberations should be informed by the protections provided for narrative research participants, the requirements of consent for genetic testing (which include a counseling process involving all appropriate family members), and a professional obligation to do no harm to the persons and families whose genetics-based stories we present.
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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 | 0.038 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.035 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".