The indigenous African cultural value of human tissues and implications for bio‐banking
Bibliographic record
Abstract
Bio-banking in research elicits numerous ethical issues related to informed consent, privacy and identifiability of samples, return of results, incidental findings, international data exchange, ownership of samples, and benefit sharing etc. In low and middle income (LMICs) countries the challenge of inadequate guidelines and regulations on the proper conduct of research compounds the ethical issues. In addition, failure to pay attention to underlying indigenous worldviews that ought to inform issues, practices and policies in Africa may exacerbate the situation. In this paper we discuss how the African context presents unique and outstanding cultural thought systems regarding the human body and biological materials that can be put into perspective in bio-bank research. We give the example of African ontology of nature presented by John Samwel Mbiti as foundational in adding value to the discourse about enhancing relevance of bio-bank research in the African context. We underline that cultural rites of passage performed on the human body in majority of communities in Africa elicit quintessential perspective on beliefs about handling of human body and human biological tissues. We conclude that acknowledgement and inclusion of African indigenous worldviews regarding the human body is essential in influencing best practices in biobank research in Africa.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".