Canadian inequality over the last 40 years: common and contrary variations on universal themes
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The Gini coefficient for disposable income for Canada in 2019 was approximately the same level it was at in 1995. Underlying this flat recent long‐term pattern is an increase in the level of market earnings inequality in the 1980s and 1990s that Canada shared with other countries followed by a continuing period of flatness in that measure as well. This trend interacted with changes in policy that have, at times, offset earnings inequality trends and at other times exacerbated them. In this paper, we describe these trends and the combination of market and policy forces that drove them. We conclude that explanations rooted in ongoing technology or globalisation forces are less relevant than explanations based on deeper structural changes in the labour market. Those changes affecting earnings inequality were ultimately fully offset by changes in the tax and transfer system as well as labour market policies such as the minimum wage.
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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 | 0.001 | 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