Bibliographic record
Abstract
Recently many commentators have expressed concern about the fiscal health of Toronto and, more generally, the Greater Toronto Area (GTA). Such concern is puzzling because most of the available evidence suggests that municipalities in the GTA are fiscally healthy. Over the past decade, for example, none have deficits in their operating budgets, none have borrowed excessively to pay for capital expenditures, none have raised property taxes significantly, none have run up large tax arrears, and all have become less reliant on provincial grants. It may be, however, that, in a more fundamental sense, the overall fiscal health of the GTA region has less to do with balancing its budget, which municipalities in Ontario are required to do by law, than with how well services are being provided and the state of municipal infrastructure. Our main aim of this paper is to analyze the fiscal sustainability of the GTA. To begin with, we first discuss in Section 1 just what is meant by fiscal sustainability at the local level and how it can be measured. We then outline in Section 2 the fiscal situation in the GTA as a whole now and over the last decade. Of course, not all parts of the GTA have had the same experience over this period, so in Section 3 we compare some key fiscal indicators for the different regions within the GTA in 2000. Against this factual background, we then move on to consider what may happen in the future. To begin with, in Section 4 we assume, essentially, that past revenue and expenditure trends and current policies will carry on unchanged for the next thirty years or so, and then summarize the outcome of such a “business as usual” scenario in terms of what would happen to property tax rates. Unfortunately, owing in part to the substantial recent changes made in municipal organization and finance in Ontario, the data needed for proper projections are simply not available, so the estimates we present in Section 4 are inevitably rather conjectural. In Section 5 we therefore consider several important additional factors that seem likely to have an impact on the tends shown in Section 4, and hence on the fiscal sustainability of the GTA in the future. Finally, in Section 6 we consider briefly what might be done by each level of government to give municipalities a new fiscal deal that would indeed improve their ability to provide the services and maintain the infrastructure needed to make the GTA city-region as a whole economically competitive and successful in the future. Section 7 contains a brief summary and conclusion.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".