Achieving Reconciliation: An Analysis on Policies Affecting the Indigenous Peoples of Canada
Bibliographic record
Abstract
Although there has been an increased focus on the importance of the Canada federal government’s use of policy to achieve reconciliation, studies show that they failed to produce meaningful change for the Indigenous population. This exploratory paper aims to analyze the Canadian governments use of policy and its framework to evaluate its efficacy and uncover what factors have potentially caused past policies to fail. A detailed examination on past and current statistics on key indicators for the Indigenous population is used to define the problem. A detailed policy analysis is then utilized to evaluate the efficacy of the policy, identify potential flaws within the framework, and provide potential solutions. The results from the policy analysis argue that the main variables that currently function as an obstacle to achieving reconciliation are a lack of political will, the absence of Indigenous methodologies, and a lack of transparency. It is suggested that to improve the outcomes of these policies and achieve reconciliation; the Canadian government must prioritize the Indigenous population and work to integrate their methodologies within the Western Framework and develop transparent plans.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".