Bibliographic record
Abstract
The Canadian government has set a high priority on reducing the economic burden of taxation. In a context of fiscal surpluses, it has been: markedly reducing corporate income and capital taxes; providing more personal tax relief especially at lower incomes and above all for saving; and cutting the federal value added tax (GST). While such measures, in particular income and capital tax cuts, reduce the economic damage caused by tax, Canada should go further along this route with significant revenue-neutral reforms to achieve a more efficient tax mix that also retains its redistributive features. Numerous tax preferences to favoured activities, firm types, investments and savings vehicles narrow the tax base and create loopholes, keeping statutory rates higher than otherwise and distorting resource allocation. They should therefore be removed. It would also help to shift the tax mix toward more user fees and indirect taxes – including VAT, environmental levies and property taxes – which do not distort inter-temporal economic choices as income taxes do. Lower corporate and personal income taxes could improve the incentives for capital formation, FDI, innovation, entrepreneurship, labour-force participation, work effort, and the pursuit of higher education. The result would be higher standards of living.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".