Bridging the Divide between Genomic Science and Indigenous Peoples
Bibliographic record
Abstract
The new science of genomics endeavors to chart the genomes of individuals around the world, with the dual goals of understanding the role genetic factors play in human health and solving problems of disease and disability. From the perspective of indigenous peoples and developing countries, the promises and perils of genomic science appear against a backdrop of global health disparity and political vulnerability. These conditions pose a dilemma for many communities when attempting to decide about participating in genomic research or any other biomedical research. Genomic research offers the possibility of improved technologies for managing the acute and chronic diseases that plague their members. Yet, the history of particularly biomedical research among people in indigenous and developing nations offers salient examples of unethical practice, misuse of data, and failed promises. This dilemma creates risks for communities who decide either to participate or not to participate in genomic science research. Some argue that the history of poor scientific practice justifies refusal to join genomic research projects. Others argue that disease poses such great threats to the well-being of people in indigenous communities and developing nations that not participating in genomic research risks irrevocable harm. Thus, some communities particularly among indigenous peoples have declined to participate as subjects in genomic research. At the same time, some communities have begun developing new guidelines, procedures, and practices for engaging with the scientific community that offer opportunities to bridge the gap between genomic science and indigenous and/or developing communities. Four new approaches warrant special attention and further support: consulting with local communities; negotiating the complexities of consent; training members of local communities in science and health care; and training scientists to work with indigenous communities. Implicit is a new definition of "rigorous scientific research," one that includes both community development and scientific progress as legitimate objectives of genomic research. Innovative translational research is needed to develop practical, mutually acceptable methods for crossing the divide between genomic researchers and indigenous communities. This may mean the difference between success and failure in genomic science, and in improving health for all peoples.
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 | 0.097 | 0.120 |
| 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 | 0.011 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.017 |
| 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; both teacher heads agree on what is shown here.
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".