Policy Forum: Improving the Canada Revenue Agency's Delivery of Social Benefits—A Practitioner's Perspective
Bibliographic record
Abstract
In Canada, the tax system has become closely intertwined with the income support system; many key income support benefits are delivered through the tax system. Other authors have identified problematic elements of the use of the tax system as a benefit administration tool, and concerns about the role of the Canada Revenue Agency (CRA) as a social benefits agency, in addition to its essential role as a tax collector, stemming in part from issues arising from Canada's response to the COVID-19 pandemic. Suggestions to enhance the tax system's ability to deliver benefits include pre-filled tax forms and real-time reporting. I suggest that, while some reforms are practical even in the short term, others require a long-term perspective, or even a shift in the philosophy of our tax system, to implement changes such as reducing the volume of deductions and credits, and accepting standard claims rather than precise calculations supported by receipts and other documentation. Additionally, given that we live in a specialized society, I suggest that these goals would be better achieved by the CRA acting in collaboration with other groups, within and outside the government, to better reach vulnerable populations and deliver the benefits that they are entitled to receive.
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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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.034 | 0.017 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".