Bibliographic record
Abstract
The objective of this report is to provide an overview of trends in income inequality, defined as the Gini coefficient, in Canada and the provinces over the 1981-2010 period and to investigate the impact of redistributive policies – namely, taxes and transfers – on these trends. Income inequality is measured in terms of market income, total income, and after-tax income, with the latter considered the most important from a well-being perspective. The main findings in this research note are outlined below: Canada’s after-tax income Gini coefficient, which measures inequality after taxes and transfers, was 0.395 in 2010, 0.123 points or 23.7 per cent lower than the market income Gini coefficient (i.e. inequality before taxes and transfers) of 0.518. Of the total 23.7 per cent reduction in the Gini coefficient, 70.7 per cent was due to transfers and 29.3 per cent was due to taxes. It is evident that Canada’s redistribution policies considerably reduce market income inequality. Between 1981 and 2010, the market Gini coefficient increased by 0.084 points, or 19.4 per cent. This growing market income inequality was partially offset by a larger dampening effect of both transfers and taxes on inequality (by 0.027 points and 0.010
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".