Natural Resource Blessing or Curse on Education and Income: A Study of Canadian Provinces
Bibliographic record
Abstract
This major research analyzes the effects of natural resource abundance in Canadian provinces on its education level and income per capita. Past studies have found that a country with poor government governance have brought natural resource curse, where large natural resource reserves have decreased the education level and economic well-being of that country. By collecting the data from the Labour Force Survey and Statistics Canada, this paper tests if natural resource abundant provinces, such as Alberta and British-Columbia have gone through natural resource curse or natural resource blessing in comparison to natural resource scarce provinces, like Ontario and Quebec. There are two regression methods used here, one following the simple regression used by most natural resource curse paper to test education level in the provinces, and one following Boyce and Emery (2011)'s income per capita regression. The first method of regression on education enrolment and government expenditure on education per capita did not return significant results, but the second following Boyce and Emery (2011)'s regression method did return that resource rich provinces enjoy higher income 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".