Bibliographic record
Abstract
There has been an increasing reliance on the user-pay model to fund municipal infrastructure in Canada. User fees as a source of own-source municipal revenue have more than tripled since 1965 (Tindal et al. 2012, p. 262). However, the application of the user-pay model is constrained by two important factors. First, user charges have very specific legal constraints on them that may be at odds with the nature of the specific infrastructure for which funds are being sought. Second, municipal government is known as the “the most varied form of government in Canada” (Treff and Ort 2013, p. 1:3) which makes it difficult to not only make comparisons of the use of user charges both within and across jurisdictions, but also to make sweeping conclusion about the best way for all municipalities to fund their infrastructure priorities. These characteristics and constraints mix together to provide a complicated landscape within which municipal infrastructure needs can be and are financed and leads to diverse approaches to financing municipal infrastructure in Canada. In addition, it explains why many municipalities may not adhere to the best practices as outlined in the literature. This paper examines some of the general considerations regarding the constraints on user charges along with specific contextual environments regarding the municipal user charges in Western Canada. The paper begins by setting out the constraints that user charges must meet regardless of jurisdiction and then considers the specific jurisdictional constraints in two Western provinces. The discussion focuses on Alberta and British Columbia as these two provinces, despite being neighbours, have very different environments within with municipalities operate leading to different reliance on and uses of user charges. The paper concludes by discussing some of the comparative complications that arise.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".