e-brief Still a Wallflower: The 2008 Report on Canada’s International Tax Competitiveness
Bibliographic record
Abstract
Business taxation is one of government’s most important policy levers for stimulating economic growth and improving the wellbeing of Canadians. In the past decade, federal and provincial governments have addressed serious shortfalls in Canada’s business tax policies, by reducing corporate income tax rates, improving the capital cost allowance system and reducing or eliminating capital taxes. This has improved Canada’s tax competitiveness, although as we show below, Canada’s rank as 11th highest among 80 countries, continues to reflect high marginal effective tax rates on capital, especially in the service sectors. This indicates the need for a serious new approach to industrial policy and tax reform, which ought to be an important topic in the current federal election. The recent slowdown in our economy is a sharp reminder to Canadians on the importance of growth. A booming business environment enables employers to take on more workers, invest in new technologies and pay higher salaries to attract workers with needed skills. Contracting economies imply employee layoffs, postponement of investment plans and lower wages. Insofar as Canadians care about economic growth, they should also care about further increasing our country’s international tax competitiveness. A competitive tax regime attracts business investment that is so crucial to improving our mediocre productivity record, raising incomes and stimulating economic growth.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".