The Impact of Self-Government, Comprehensive Land Claims, and Opt-In Arrangements on Income Inequality in Indigenous Communities in Canada
Bibliographic record
Abstract
In Canada, self-government agreements, comprehensive land claims agreements, and opt-in arrangements allow Indigenous groups to govern their internal affairs and assume greater responsibility and control over the decision-making that affects their communities. We use difference-in-difference models to measure the impact of such agreements on average income and income inequality in Indigenous communities at the community level. In comparison with earlier work, we additionally use data from the 2016 Census. Our results suggest that comprehensive land claims agreements increase community-level average (log) household incomes by more than C$10 thousand (0.25 log points). Attainment of other agreement types does not increase community-level average incomes. Communities that attain a self-government agreement or an opt-in arrangement related to land management see a decrease in the Gini coefficient for income inequality of 2.0 to 3.5 percentage points. Standalone comprehensive land claims agreements are associated with a smaller decrease of 1.2 percentage points. We also study intergroup inequality and find that an opt-in arrangement increases within-community income disparity between Indigenous and non-Indigenous households.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".