Indigenous Peoples and genomics: Starting a conversation
Bibliographic record
Abstract
Compared to European ancestral groups, Indigenous Canadians are more likely to have uninterpretable genome-wide sequencing results due to non-representation in reference databases. We began a conversation with Indigenous Canadians to raise awareness and give voice to this issue. We co-created a video explaining genomic non-representation that included diverse Indigenous view-points. We audio-recorded the focus groups including 30 First Nations, Métis, and Inuit individuals living in Greater Vancouver. After watching an introductory video explaining genomic testing, participants discussed issues surrounding collecting Indigenous genomic data, its control, and usage. Transcripts were analyzed, and participants' quotes representing main themes were incorporated into the introductory video. Indigenous participants discussed data interpretation and gave approval for quote usage. The 20 participants who provided feedback concurred with the thematic interpretation: Systemic racism interlaced most conversations, particularly within the theme of trust. Themes of governance emphasized privacy and fear of discrimination. Some participants thought a separate, Indigenous-controlled database was essential; others recognized advantages of international databases. The theme of implementation included creative ideas to collect Indigenous genomes, but prior approval from Indigenous leaders was emphasized. The final video (https://youtu.be/-wivIBDjoi8) was shared with participants to use as they wish to promote awareness and ongoing discussion of genomic diagnostic inequity.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.065 | 0.024 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".