Frontiers of Bio-Decolonization: Indigenous Data Sovereignty as a Possible Model for Community-Based Participatory Genomic Health Research for Racialized Peoples in Postgenomic Canada
Bibliographic record
Abstract
This paper explores the manners in which Indigenous and allied non-Indigenous researchers, medical directors, and knowledge-keepers (among others) extend the ethical precepts and social justice commitments that are inherent in community-based participatory research (CBPR) approaches to genomics. By means of a genealogical analysis of bioethical discourses, I examine the problem in which genomic science claims to offer potentially beneficial genetic screening tools to Indigenous and racialized peoples who have and continue to struggle with historical health inequity, exploitation, and exclusion by the very biomedical institutions which would be charged with the task of ethically introducing these biomedical tools. This investigation focuses on Indigenous data sovereignty (IDS) as an approach established by Indigenous communities and scientists to gain access to the benefits of genomic health which, if the field’s promises are true, aims to counter the historical neglect or exploitation by biomedical researchers and institutions. I chart the role of CBPR principals as it pertains to collective efforts by both Indigenous communities and non-Indigenous allies to create the social, biomedical, and institutional conditions to improve Indigenous health equity in the context of genomic science in two specific studies: the Silent Genome initiative (British Columbia) and the Aotearoa Variome (Aotearoa/New Zealand). This investigation contributes insights to social science literatures in health equity for racialized communities, biomedical ethics, Indigenous Science and Technology Studies, and decolonial biomedical and technoscience histories.
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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 | 0.071 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.116 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.005 | 0.010 |
| 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".