Policy Forum: Using Retributive Justice To Ensure Public Trust in Canada's Tax System
Bibliographic record
Abstract
To ensure public trust in the tax system, retributive justice must be present. That is, tax offenders must be held accountable for their unethical behaviour and punished in accordance with tax laws. In this article, the authors first summarize recent academic research about the deterrent effect of knowledge of others' tax-crime punishments. They classify this research into two categories: archival "naming-and-shaming" research and experimental tax ethics research. Next, they report the results of a survey of adult Canadian taxpayers in which they assess the perceived severity of tax-crime punishments. They compare these survey results with aggregate tax-crime punishment data in Canada and show that there is an expectations gap between what Canadians believe constitutes appropriate punishment for tax crimes and actual punishments. Finally, the authors discuss enforcement and other challenges for tax authorities in bringing about retributive justice, and consider other ways in which tax authorities might make tax offenders accountable for their crimes. Their ultimate suggestion is that the Canada Revenue Agency needs to increase its perceived power by increasing the frequency of actual prosecutions for tax evasion. Recent proposals for reform of the general anti-avoidance rule may lead to more prosecutions.
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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| 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".