Policy Forum: The Tax Gap from the Perspective of Tax Practitioners
Bibliographic record
Abstract
The Canadian Tax Foundation, with assistance from the Canada Revenue Agency (CRA), organized a round table, held in Ottawa on June 6, 2017, to discuss the benefits, the drawbacks, and the many challenges that arise in estimating a tax gap. The round table was attended by senior staff members with deep knowledge and experience in this area, from a number of governments and government-sponsored agencies, including the United States, the United Kingdom, Denmark, Sweden, the Organisation for Economic Co-operation and Development, and the International Monetary Fund. Also in attendance were several prominent academics, a relatively small group of tax practitioners, and senior officials from the CRA. In this article, the authors, who participated in the round table, share the insight and knowledge they gained from listening to and reading about the experience of foreign and Canadian governments and agencies with tax gap estimation.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".