Biobank/Genomic Research in Nigeria: Examining Relevant Privacy and Confidentiality Frameworks
Bibliographic record
Abstract
Nigeria's commitment to genomic research and biobanking is beyond dispute. Proof, if there is need for one, is that the country is one of only six nations (others are Canada, China, Japan, the United Kingdom, and the United States) involved in the International HapMap Project. The HapMap Project is an innovative enterprise aimed at developing a haplotype map of the human genome, a tool that is helpful to studying the genetic basis of disease as well as the genetic or hereditary factors that contribute to variation in response to environmental factors, in susceptibility to infection, and in the effectiveness of, and adverse responses to, drugs and vaccines. In addition, the country is home to H3Africa biobank (with 45, 358 human samples in storage), affiliated with the Institute of Human Virology of Nigeria (IHVN), and several others. Benefits accruing from genomic research and biobanking are enormous; so also is protection of research subjects. The protection envisaged centers primarily on, inter alia, securing informed consent, safeguarding privacy and maintaining confidentiality of health information - all of which are enshrined in ethicolegal regimes in Nigeria. But whether these frameworks are consistent with international best practices is not at all clear, hence the need for this paper.
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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 | 0.055 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.040 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".