Generating Social Capital In First Nations: Learnings from the USIC Project
Bibliographic record
Abstract
Social capital has become a much-used phrase in academic literature to describe relationships of trust that evolve between partnering organizations, individuals, governments and academics. Using a case study approach this paper explores the mobilization of internal and external networks that occurred in the "Understanding the Strengths of Indigenous Communities" (USIC) project1 to uncover some considerations for the generation of social capital within First Nations. The paper identifies some key factors to consider in the development of social capital in First Nations, including using strengths - rather than deficits. This entails respecting and including a diversity of perspectives and community members and establishing processes and protocols for relationships both within the community and with external partners and organizations. The paper concludes that building cross-cultural networks requires time, patience, perseverance, and effort, and will be constantly challenging. However, these networks may also benefit the collective interests of First Nations by encouraging community engagement and power-sharing within 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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".