Policy Forum: Building a Tax Review Body That Is Fit for Purpose--Reconciling the Tradeoffs Between Independence and Impact
Bibliographic record
Abstract
In this brief opinion piece, I discuss various options for structuring and supporting a review of the federal tax system in Canada, including interactions with subnational tax measures. I focus in particular on the tradeoffs that must be made between independence from government and influence on government policy making. The traditional models that we are accustomed to thinking of -- internal reviews by government officials, task forces, and royal commissions -- all have strengths and weaknesses as mechanisms for conducting a comprehensive, transparent, independent, and influential review of the tax system. In addition to these options, I consider international examples, such as the United Kingdom's Mirrlees review. In the end, I suggest the creation of a publicly funded body with a standing mandate to conduct analysis, engage stakeholders, and make policy recommendations. Tax reform is unlikely to be a one-time policy need in Canada, and so we should build a tax review body fit for purpose.
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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 | 0.245 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.034 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".