Legitimate Expectations in Canada: Soft Law and Tax Administration
Bibliographic record
Abstract
This chapter examines the relationship between legitimate expectations and soft law. In what circumstances can an agency’s guidelines create law — or at least legally enforceable expectations? At first glance, the answer would appear obvious. The key reason for developing soft law is to provide guidance and transparency as to the process (and sometimes the substance) of administrative action. Soft law by its nature gives rise to expectations. Whether those expectations, in turn, give rise to legal effects is decidedly less clear. In fact, this question has vexed Canadian administrative law. Nowhere are questions of soft law and legitimate expectations more salient than in the context of tax administration.\nWe canvass the relationship between legitimate expectations and soft law in the context of Canadian tax administration. The analysis proceeds in three parts. In the first part, we consider the important roles of soft law in a tax administration system premised on self-assessment. Within this analysis, we list and describe six sources of soft law in the tax administration context. In the second part, we explore the development of the doctrine of legitimate expectations in Canada, and the implications of the Supreme Court of Canada’s (SCC) most considered treatment of soft law and legitimate expectations in Agraira v Canada. The third part of the chapter analyses when (and pursuant to which principles) soft law in the tax administration context (eg information circular, interpretation bulletin, or advance judgment) may give rise to a legitimate expectation.\nWe conclude that Canadian administrative law has only begun to grapple with legitimate expectations, and that its development in the context of soft law represents an important catalyst for sorting out a more coherent and transparent framework for the review of administrative action.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".