Bibliographic record
Abstract
INCOME GAPAccounting for a widening U.S.-Canada income gap D espite recent improvements, real disposable income per capita in Canada is still Can.$400lower than its level in 1989.This is significantly different from the trend obser ved in the United States, where real per capita disposable income has risen by about U.S.$2,400.This weak income performance in Canada requires a closer examination. THE DIRECT IMPACT OF TAXATIONThe high personal income tax rates in Canada, relative to the United States, are clearly an important factor to consider when analyzing the income gap between the two countries.Indeed, Canadians pay a larger percentage of their income in taxes and other transfers to governments.As of 1999, close to 25 cents of each dollar earned in Canada went to the various governments.This compares with only 19 cents in the United States.Over the decade, Canadians also saw the rate at which they transferred their income to governments rise faster than in the United States.Since 1989, transfers to governments, as a share of personal income, rose by close to 16 percent in Canada and by 13 percent in the United States.However, the direct impact of taxes did not explain all, or even most, of the increase in the income gap between the United States and Canada over the decade.One way of showing this is to compare pre-tax (gross) income and posttax (disposable) income in both countries.Since 1989, real gross income per capita in Canada rose by only 2.1 percent or Can.$500, while in the United States it rose by 20.6 percent or U.S.$2,850.This 18.5 percent performance gap is relatively close to the 20.0 percent performance gap observed for disposable (after-tax) income.Thus, the direct impact of taxation in accounting for the increase in the income gap was comparatively minor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".