User Fee Design by Canadian Municipalities: Considerations Arising from the Case Law
Bibliographic record
Abstract
Municipal governments in Canada have come to rely increasingly on user fees to fund local services, as they struggle to deal with the combined pressures of federal and provincial devolution of responsibility for such services and the political costs of raising property taxes. While there is a substantial body of literature regarding the rationale for user fees, little information exists about how to design and implement a user fee so as to ensure that it satisfies the legal requirements for imposing this type of levy. The authors provide a detailed review of the existing Canadian case law to highlight key legal, technical, and administrative issues facing municipalities in designing and implementing user fees. The discussion focuses in particular on the principal legal tests for user fees and the application of those tests in specific cases. Through their analysis, the authors draw attention to several unresolved issues and inconsistencies in the application and interpretation of the tests, which need to be navigated and addressed by the courts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".