Bibliographic record
Abstract
In a survey of tax reform in recent years, Richard Bird and Michael Smart explore the relationship between tax policy and tax research. They conclude that there have been important examples of apparent influences of research on policy. For instance, they are encouraged that the downward pressure on personal and corporate taxes has certainly been supported, if not initiated, by the increasing evidence of distortions caused by high marginal tax rates. In their view, the adoption of the GST can be explained by the acceptance of the federal government of the economic argument that Canada had to switch to a value-added tax to reduce economic distortions. On the other hand, they are disappointed that the equally convincing economic studies of the damage done by poorly-designed excise, property and payroll taxes do not seem to have had any effect. Consequently, they believe that political economy factors were probably the more dominant explanation of the tax reforms than the simple acceptance of advice from economists. Their conclusion is that if economists want to have a greater influence on policy, they need to pay more attention to the issues that motivate policymakers, including, most notably, distributional issues, and they need to write in a way, and in a forum, that will most likely come to the notice of the policy-makers.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.047 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".