Genomics, Insurance and Human Rights: Is there a Place for Regulatory Frameworks in Africa?
Bibliographic record
Abstract
Abstract This article examines the human rights dimension of genetic discrimination in Africa, exploring the place of regulatory frameworks while taking into account the disadvantaged position of the average African. This is in response to the tendency of insurance companies toward making health insurance decisions on the basis of individual genetic information, which could result in genetic discrimination or health insurance discrimination based on a person's genetic profile. The author considers such questions as the intersection between human rights (right to life, health, privacy, human dignity and against genetic discrimination) in relation to the insurance industry, as well as the obligations of state and non-state actors to promote, respect, and protect the enjoyment of these rights. The article argues that African nations should not stand aloof in trying to balance the competing interests (scientific, economic and social) presented by the use of genetic information in the health care context and that ultimately it is the responsibility of states to develop domestic policies to protect their most vulnerable citizens and to prevent entrenched private discrimination based on an individual's genes.
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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 | 0.014 | 0.013 |
| 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 | 0.052 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".