Bibliographic record
Abstract
Some economists have attacked the two-percentage point cut in the GST to 5 per cent proposed by the Conservatives in the January 2006 Canadian federal election. The main reason for this is that many economists believe that, if money was available for tax cuts, it would make more sense to use it to lower income taxes than the GST. This is because the personal income tax is, in theory, a relatively inefficient tax that penalizes savings. In practice, however, the income tax does not penalize savings as much because of the prevalence and widespread use of tax deductible savings plans and a new Tax-Free Savings Plan that can be used as an additional way to shelter interest income and that make the income tax more like a more efficient consumption tax. A neglected additional advantage of the GST cuts is that the lower GST rate they establish made it easier to achieve an agreement to harmonize provincial sales taxes with the GST, which is what happened in Ontario and British Columbia (although B.C. subsequently backed out). A lower 5-per-cent GST rate is becoming an accepted fiscal fact of Canadian life and is unlikely to be reversed.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".