Frontiers of Bio-Decolonization: Indigenous Data Sovereignty as a Possible Model for Community-Based Participatory Genomic Health Research for Racialized Peoples in Postgenomic Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.019 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it