Policy Forum: Re-Envisaging the Canada Revenue Agency—From Tax Collector to Benefit Delivery Agent
Bibliographic record
Abstract
In Canada, the tax system is not used just to raise revenue; it is also an important instrument for achieving various social policy objectives. As a result, the tax system has become closely intertwined with the income support system; it now serves as the delivery mechanism for many key income support benefits. As a benefit administration tool, the tax system has advantages, but it is also problematic. First, it relies on self-assessment, which means that the onus is on individual taxfilers to provide complete and accurate information to the government on the income taxes that they owe. However, persons who have no tax liability are not legally required to file tax returns, and therefore many may not do so. In the context of benefit delivery, the reliance on self-assessment risks missing many individuals who are eligible to receive income benefits. Second, individuals generally file a self-assessed tax return only once a year. As a result, the tax system could not respond to the dramatic in-year income shocks that occurred during the COVID-19 pandemic. We identify ways to modernize Canada's tax system and make it more responsive and streamlined for the purposes of benefit delivery. Reforms such as pre-filled tax forms and real-time reporting could greatly improve the ability of the Canada Revenue Agency to deliver income benefits, and the ability of the federal and provincial governments to meet social objectives, including those set out in Canada's and the provinces' poverty reduction strategies.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.027 | 0.020 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".