Unresolved Controversies in Suing for Negligence of Tax Officials: Canadian and Australasian Insights and a Primer for Policy Makers' Consideration
Bibliographic record
Abstract
There have been numerous recent Canadian cases in which taxpayers have alleged negligence by Canada Revenue Agency officials. This body of rapidly evolving Canadian case law constitutes, at present, the most extensive jurisprudence in the common-law world considering the tortious liability of tax officials. It also exposes fundamental unresolved controversies that inhibit legal clarity and certainty on the limits of the right of taxpayers to sue for the negligence of tax officials. Through comparison with cases in Australia and New Zealand, this article confirms that these unresolved controversies are not unique to Canada. The author proposes a range of options for addressing these issues. Intended as a primer for policy makers' attention and debate, these proposals are drawn from judicial and legislative approaches adopted in Canada, Australia, and New Zealand, and in other broadly comparable common-law jurisdictions.
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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.031 | 0.076 |
| Scholarly communication | 0.029 | 0.020 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.027 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".