“We all know each other”: A Strengths-based Approach to Understanding Social Capital in Pictou Landing First Nation
Bibliographic record
Abstract
With over three decades of attention drawn to the health of Indigenous peoples in Canada and around the world, an outpouring of health research has been undertaken, much of which has emphasized the experience of disparity at the expense of recognizing strengths. In this case study, we challenge the damage-centred rhetoric of mainstream health research by reporting the findings of 20 qualitative interviews on community strength and health with members of Pictou Landing First Nation, a Mi’kmaw nation located in Nova Scotia, Canada. We then relate and compare these findings with the emerging conceptualization of Indigenous social capital, which is a concept that has been associated with positive health outcomes in a variety of contexts. Our findings indicate that Pictou Landing First Nation is strengthened by qualities of familiarity, reciprocity, safety, and solidarity, which are rooted in the value of family and embedded within a broader Mi’kmaw worldview. The nature of these strengths aligns in part with the concept of Indigenous social capital, which we suggest may be better harnessed to be a means for conducting strengths-based health research. To this end, our findings support the need for reworking social capital conceptualizations to more strongly centralize cultural identities and worldviews in order to authentically and comprehensively affirm Indigenous and decolonizing health research practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.021 | 0.057 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".