Bibliographic record
Abstract
It is undeniable that the goods, services, and privileges that municipalities provide are vital for Canadians’ well-being and that municipalities are facing increasing pressure to provide more infrastructure and services to more Canadians at a higher level of service. In order to provide this infrastructure and these services, however, municipalities have to increasingly find a way to pay for them. The key challenge then becomes, how do cities pay for these services and infrastructure? How do cities raise enough revenue to deliver these high-quality public services that will attract and retain businesses and residents in a way that does not undermine their competitive advantage and that is fair, accountable, equitable, and within their authorities? As it turns out the answer to this question is “wherever possible, charge.” That is, where possible, the direct users should pay the cost of providing municipal services. The rest of this chapter will outline what are the two main funding choices, property taxes and user levies, for Canadian municipalities and why. If the choices for municipalities for its own source revenues are between property taxes and user levies, what are these instruments? If the choice then is between user levies and property taxes, what has been the take up of these revenue instruments by municipalities in Canada and what might be driving these decisions? Finally, which revenue tool is preferred and why, using the principles tax fairness, tax accountability, and tax equality? In essence, it boils down to establishing a strong link between expenditures and revenues, leading to a preference for user levies.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".