First Nation paths to well-being: lessons from the Poverty Action Research Project
Bibliographic record
Abstract
This paper describes a poverty reduction approach to addressing an important determinant of health and well-being among Canada's First Nations. The Poverty Action Research Project (PARP) has its origins in the Make Poverty History Committee established by the Assembly of First Nations (AFN) in 2008. Academic members of the Committee in cooperation with the AFN subsequently applied for an action research grant to the Canadian Institutes of Health Research (CIHR). The project selected five volunteer First Nations from different parts of Canada, hiring a coordinator in each, undertaking background research, developing a profile and working with First Nation representatives in the development of a strategy to address upstream determinants of health and well-being. Subsequently, project team members within each region assisted where needed with plan implementation, supporting some initiatives with small grants. This paper provides insights from the project in several key areas, including First Nation rejection of the concept of poverty as usually defined, the importance of taking action to strengthen collectivities as well as individuals, the feasibility of assisting First Nations who are at different points in their development journey, the strengths of the leadership within the First Nations, and finding the appropriate balance between the elected and business leadership. These insights emerged from dialogue and reflection among project team members and community participants over the life of the project. We also describe what we have learned about how to engage effectively and with mutual respect with First Nations in this kind of project. The paper concludes with a review of our experiences with the policies and practices of the national research granting councils and the universities, which have not fully adjusted to the requirements of action research involving First Nations.
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.
How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".