The Politics of Trust and Participation: A Case Study in Developing First Nations and University Capacity to Build Health Information Systems in a First Nations Context
Bibliographic record
Abstract
Recent success of First Nations involvement in health information management is establishing the social and cultural structures necessary to build trust and participation, produce counter knowledges that decolonize the health of First Nations Peoples, develop new forms of health information systems directed at First Nations wellness, and create new institutional research partnerships that could further enhance health information development and educational opportunities. This success is illustrated through a number of initiatives jointly developed and managed by Manitoba First Nations Centre for Aboriginal Health Research and the Assembly of Manitoba Chiefs Health Information and Research Committee. Alternative discourses are possible. Resistance in the form of counter discourses can produce new knowledge, speak new truths and constitute new powers such as First Nations ownership, control, access and possession of health information. In this new environment, non-Aboriginal researchers and governments will have to recognize that any work involving Aboriginal Peoples will occur in the context of resistance to colonization. However, that such resistance creates the possibilities for collaboration. For collaboration to be possible and successful, however, researchers will have to reflect on the positions represented by others, attempt to understand these positions within the context they occur, recognize that trust and participation is conditional, and accept that any sharing and production of health information will occur at the boundaries between systems of knowledge.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.056 | 0.024 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".