Life after Jarvis—Just How Much Help Must You "Voluntarily" Give the Canada Revenue Agency?
Bibliographic record
Abstract
Society recognizes that privacy rights must be compromised to allow the State to administer and enforce an efficient and effective income tax regime. The question is — just how great should that compromise be? How much financial information should the State be allowed to require from its constituents to prepare, maintain and disclose on a "voluntary" basis for income tax purposes? Most importantly, for what purposes should this information obtained by the State be legitimately used, given the Charter and the criminal law privacy protections contained therein? In particular, can the Slate use its mandatory compliance powers to obtain information which would then be used to further a criminal investigation? Where is the line drawn? Although the 2002 Supreme Court of Canada decision in Jarvis provides some clarification and guidance, it does not go far enough in setting out the proper balance between a person s right to privacy and the State's need for disclosure in the income tax context. The purposes of this article are: (a) to provide a brief overview of a person's obligations to voluntarily provide both information and assistance to the State as part of the operation of the income tax regime, (b) to critically analyze the Jarvis decision in conjunction with previous jurisprudence to gain insight as to when the State will lose the ability to compel a person to assist it in its administration and enforcement duties, (c) to examine some post-Jarvis decisions to see how the State and the courts have responded to and applied the principles as set out in Jarvis, and (d) to provide some suggestions on how taxpayers, advisors and the Canada Revenue Agency might approach matters of this nature in the future.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".