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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".