Canada and the United States: Intent, Results, and Consequences
Bibliographic record
Abstract
The U.S. and Canadian tax systems are often compared, particularly in Can-ada. The comparison is usually made between the two federal tax systems, and little attention is paid to subnational-provincialktate and local-tax sys-tems in that context. Yet subnational tax systems collect an important share-40 to 50%-of overall tax revenues in both countries and are, therefore, likely to have an impact on economic choices. Accordingly, this paper presents the subnational tax systems of the two countries and, in particular, examines the degree of harmonization within and between countries, for recent years. This project should be of interest, since there has been little, if any, comparative quantitative assessment of the degree of harmonization of subnational tax sys-tems in Canada and the United States. The paper is divided into five parts. In the first, we address some defini-tional and methodological issues. In the second, we present the key features and importance of subnational tax revenues in Canada and the United States. In the third, we examine for three major taxes-personal income, corporate
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".