Rooted in Traditions: How Indigenous Nations Use Culture to Shape Policy
Bibliographic record
Abstract
The ways of knowing, doing, and being that have historically governed Indigenous Nations in Canada have been suppressed through processes of colonisation, limiting the ability of Indigenous peoples to govern themselves according to their own cultural values and practices. Policy development is an essential component of Indigenous self-governance, yet little research has examined how Indigenous policy makers are currently developing policy. Thus, this research uses a case study methodology to explore the policy development practices of three Indigenous organisations across Canada. The data collected included interviews, focus groups, documents, and publicly available information, which were analysed using Thematic Analysis to describe key aspects of each organisation’s policy development. Across case studies, community engagement was heavily favoured, and was often treated as ceremony. However, the extent to which cultural values and protocols were embedded into policy varied depending on the type of policy being created. A major barrier to policy development was a lack of capacity, which often stemmed from policy developers balancing dual roles as service providers and policy developers. This research provides insight into the policy development practices currently being used by Indigenous policy makers, and may support the ongoing efforts of Indigenous Nations to advance self-governance.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".