Indigenous Peoples, Social Policy, and the Challenges of Reconciliation
Bibliographic record
Abstract
Abstract This chapter examines the persistent socio-economic disparities between Indigenous and non-Indigenous peoples in Canada, linking them to the historical entanglement of the welfare state with assimilationist policies. This legacy has produced a segmented social policy regime in which Indigenous peoples—particularly First Nations living on reserves and Inuit in remote regions—receive underfunded and jurisdictionally fragmented services. These inequalities are further exacerbated by systemic discrimination and chronic infrastructure deficits. Focusing on health and education, the chapter illustrates how jurisdictional gaps and standardized service models systematically fail to address the distinct needs and priorities of Indigenous communities. It then analyzes recent developments in child welfare policy framed by the language of reconciliation that signal a shift toward needs-based funding, substantive equality, and the recognition of Indigenous self-determination. While these initiatives represent important steps forward, their impact remains constrained by top-down implementation approaches and persistent federal-provincial tensions. The chapter concludes that meaningful reconciliation in social policy requires a fundamental transformation of intergovernmental relations and institutional practices—one that positions Indigenous nations as equal partners with the authority to define and deliver social programs in accordance with their own understandings of well-being.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".