The Labour Market and Economic Performance of Canada’s First Nations Reserves: The Effect of Educational Attainment and Remoteness
Bibliographic record
Abstract
The goal of this report is to investigate the relationship between educational attainment, remoteness, and labour market and economic performance at the reserve level for Aboriginal Canadians. The report uses reserve-level data on average earnings, GDP per capita, labour market indicators and distance to a service centre for 312 reserves. Using descriptive statistics, simple correlation and multiple regression analysis, the report draws conclusion on four important questions. First, the report finds that a higher level of educational attainment, on average, has a positive effect on the labour market performance of a reserve. Then, a positive link is found between educational attainment and economic performance (average earnings and GDP per capita). Also, the report finds evidence that remoteness of a reserve plays a role in its labour market and economic performance. Specifically, reserves situated near urban centres fare better than the ones in rural/remote areas and those not connected by road to a service centre all year long (special access). However, when controlling for characteristics of reserves, the very remote reserves seem to fare better than expected in comparison to urban reserves. Yet, when an instrumental variable is used to account for the possibility that educational attainment is endogenous in the model, the remoteness of a reserve appears to play no role in determining reserve labour market or economic performance. Finally, the report also analyses the role of governance on labour market and economic performance. It finds that better governance is correlated to better labour market performance, higher average earnings and higher GDP per capita.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".