REGIONAL DISPARITIES IN CANADA: INTERPROVINCIAL OR URBAN/RURAL?
Bibliographic record
Abstract
The nature of regional disparities in Canada is analysed in this paper, with a focus on their interprovincial or urban/rural nature. Starting by presenting a traditional approach to regional disparities in Canada, we show that statistics indeed lead us to believe that there are important interprovincial disparities in Canada. Using the “Modified” Beale Codes approach which divides census divisions into more or less urban/rural categories, we then produce econometric results which again confirm the presence of inter-provincial disparities, but also of urban/rural disparities in Canada. If we test for the presence of interprovincial disparities amongst only similar census divisions rather than all census divisions, we arrive at the conclusion that a certain amount – but by no means all – regional disparities in Canada are indeed urban/rural disparities rather than interprovincial disparities and that these interprovincial disparities are less important than initially thought. Our results are very important for policy development. Principally, the fact that some provinces are lagging other in socio-economic measures may have more to do with the relative level of urbanity or rurality present in these provinces, rather than of better or worst policies, labour forces, entrepreneurial spirit, etc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".