Collective intelligence to solve complex health challenges facing Indigenous peoples: organ donation and transplantation
Bibliographic record
Abstract
The First Nations and Métis Organ Donation and Transplantation Network (the Network) facilitates Indigenous-driven, culturally-informed, and safe research, policies, education, and advocacy regarding organ donation and transplantation through the building of collective intelligence among Indigenous peoples in Canada. The Network’s think tank comprises Indigenous Elders, thought leaders, and persons with lived experiences of organ donation—living donors and organ recipients—as well as healthcare professionals, outreach workers, and university-based researchers. The Network responds to the failure of governmental institutions to reduce health disparities facing Indigenous peoples, and the dispersal of Indigenous collective intelligence caused by changing federal or provincial and territorial leadership and priorities. The collective intelligence of Indigenous peoples regarding end-stage organ failure and organ donation and transplantation is central to improving patient experiences, increasing the number of Indigenous organ donors and recipients, and finding pathways for advancing healthcare reforms that prevent and treat end-stage organ failure.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".