RURAL INCOME DISPARITIES IN CANADA: A COMPARISON ACROSS THE PROVINCES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One objective of public policy is to reduce income disparity in Canada. Previous research (e.g. Rupnik, Thompson-James and Bollman (2001)) has indicated that, on average, rural residents have a similar incidence of low income as urban residents. However, there is considerable diversity within rural regions, i.e. the term “rural” is far from being a homogeneous entity. For example, the rural regions in Ontario are very different from the rural regions in the Prairies due to the differences in population size and access to markets, among other features. Since rural regions across Canada differ economically and socially, it follows that the nature of rural income disparities could also differ across provinces in Canada. The objective of this study is to describe the range in income disparities across rural Canada. We will address two aspects:
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it