Exploring Indigenous Social Capital and Health Among Four First Nations: A Strengths-Based Study
Bibliographic record
Abstract
The impact of social networks and relationships on health outcomes has been acknowledged and studied within the concept of “social capital” in the field of public health for over two decades. To date, however, few research studies have included Indigenous peoples, and even fewer have focused on the unique strengths that Indigenous social environments bring to health and well-being. In view of these observations, the goal of my thesis is to explore the concept of social capital and its effects on health within Indigenous paradigms of community, health, and wellness. This thesis is framed around a mixed methods study, drawing from a survey that was conducted as part of the Canadian Alliance for Healthy Hearts and Minds study, as well as from interviews with members of Pictou Landing First Nation. Together, the findings of this study suggest that individual Indigenous communities possess unique stocks of social capital that are distinct from one another, as well as fundamentally different from those of non-Indigenous communities. This study also identifies the intimate relation between the construction of social capital and cultural ontologies, histories, and identities, and challenges the use of non-Indigenous (or pan-Indigenous) metrics of social capital in health research. Ultimately, this study calls for stronger consideration of Indigenous social capital as an asset in public health and health promotion involving Indigenous peoples and communities.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".