Assessment and collection of corporate income tax in Quebec, Ontario and Alberta : the problems of an independent approach in a federal jurisdiction
Bibliographic record
Abstract
Seven provinces in Canada have entered tax collection agreements with the federal government whereby that government collects corporate income tax on their behalf. Quebec, Ontario and Alberta have not entered such agreements and levy and collect corporate income tax pursuant to their own legislation and within their own administrative systems. This thesis will examine the problems resulting from the independent approaches taken by Quebec, Ontario and Alberta, as they affect the corporate taxpayer. The problems fall into three categories. First, provincial adoption of the Income Tax Act (Canada), while assuring some similarity between the federal and provincial systems, can have adverse consequences for the corporate taxpayer. Secondly, differences between the legislation of Canada, Quebec, Ontario and Alberta create inconsistencies that present difficulties for the corporate taxpayer. Thirdly, differences in the administrative systems of the three provinces and the federal government increase the cost to the corporate taxpayer and create compliance problems for it. The thesis concludes that the future of the Canadian corporate income tax system will involve even more provincial independence and, therefore, measures to alleviate some of the problems are discussed. These include a new approach to co-operative federalism, an examination of the efficacy of more provincial autonomy and tax harmonization. This analysis shows that the corporate taxpayer would benefit from more cooperation between the federal and provincial governments together with a degree of harmonization of the tax bases.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".