Engaging Maori in Biobanking and Genetic Research: Legal, Ethical, and Policy Challenges
Bibliographic record
Abstract
Publically funded biobanking initiatives and genetic research should contribute towards reducing inequalities in health by reducing the prevalence and burden of disease. It is essential that Maori and other Indigenous populations share in health gains derived from these activities. The Health Research Council of New Zealand has funded a research project (2012-2015) to identify Maori perspectives on biobanking and genetic research, and to develop cultural guidelines for ethical biobanking and genetic research involving biospecimens. This review describes relevant values and ethics embedded in Maori indigenous knowledge, and how they may be applied to culturally safe interactions between biobanks, researchers, individual participants, and communities. Key issues of ownership, privacy, and consent are also considered within the legal and policy context that guides biobanking and genetic research practices within New Zealand. Areas of concern are highlighted and recommendations of international relevance are provided. To develop a productive environment for "next-generation" biobanking and genomic research,"‘next-generation" regulatory solutions will be required.
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 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 | 0.078 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".