Bibliographic record
Abstract
In the third paper on taxation, Jack M. Mintz and Thomas A. Wilson consider the best way to allocate the “fiscal dividend”. This is the amount available to the government that can be used for tax cuts or expenditure increases within the framework of a balanced budget. In their view, although the current growth recession will reduce the potential surplus somewhat, the medium-term outlook is still for increasing surpluses. Concerned about lagging economic growth and emphasizing the importance of efficiency and productivity growth, they argue that priority should be given to debt reduction and tax cuts designed to stimulate investment and potential growth. Mintz and Wilson make the case that a large part of the remaining fiscal dividend should be allocated towards reducing the relatively large personal income tax burden faced by many Canadian families and individuals. But they also stress that it is important to steadily reduce payroll and business taxes as well. This case is supported by extensive international comparisons of taxes in Canada with other countries that show that the burden of taxation is higher in Canada than in many other industrialized countries. It is also bolstered by the results of simulations, using the FOCUS macroeconometric model, of a fiscal package containing significant debt reduction, modest spending increases and cuts in personal, business and payroll taxes. These simulations show such a fiscal package should have favourable supply-side effects on output, employment and productivity over the medium term. In addition, since their analysis reveals that there are still important issues of tax structure that need to be addressed, they recommend that the government establish a task force to review personal income taxes and to consider the need for additional tax cuts. Finally, Mintz and Wilson also remind us that while planned debt reduction is an important component of a growth-oriented fiscal policy, in the short run the size of the surplus should be allowed to vary with the level of economic activity. Otherwise fiscal policy will exacerbate the slowdown that is currently underway.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".