A POLICY RESPONSE TO CANADIAN ECONOMIC INEQUALITY A POLICY RESPONSE TO CANADIAN ECONOMIC INEQUALITY
Bibliographic record
Abstract
Economic inequality is increasing in Canada and throughout the world. In addition to the equity concerns of distributive justice, growing economic inequality negatively impacts poverty, social cohesion, and the stability of the economy. This master’s thesis undertakes a major literature review to explore the trends in economic inequality and the policies that influence it. The current increase in economic inequality has been dominated by an increase in the income and wealth of the 1 % to which the Occupy movement has drawn significant attention. Policies to directly counter this rise in both before-and after-tax top incomes are critical to combatting economic inequality. In addition to highlighting policies that target the very rich, this thesis examines intersections between traditional social policy and broader public policy in the field of economic inequality. It also argues for increased consideration of economics in social work research and policy practice. Economic inequality should be a concern to social workers alongside poverty. Policies in four areas are considered: income taxes and transfers, public services, labour market institutions, and capital market interventions. Recommendations are made for the future. Addressing economic inequality through national policy is both possible and advantageous. A comprehensive policy package involving policies from the four areas explored has the potential to reduce economic inequality. iv
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".