Bibliographic record
Abstract
Local fiscal autonomy depends on the extent to which local governments rely on their own source revenues rather than intergovernmental transfers and also on their ability to set their own tax rates. Compared to other major cities around the world, Toronto appears to have a lot of local autonomy because it is less dependent on intergovernmental transfers and more reliant on property taxes and user fees. Nevertheless, its autonomy is limited by provincial government restrictions on how these taxes and fees are levied and also by the conditions imposed on federal and provincial transfers. Toronto also has fewer revenue-raising options than many other cities. This paper describes the extent to which Toronto enjoys fiscal autonomy and evaluates the degree to which it takes advantage of the autonomy it has in setting tax rates and making other financial decisions. The paper considers some of the fiscal challenges the city now faces and some future challenges on the road ahead. It evaluates the extent to which the city can respond to these various external shocks with the resources at its disposal. It concludes that, although Toronto enjoys some local fiscal autonomy, it would benefit from having more diversified revenue sources. This paper describes the extent to which Toronto enjoys fiscal autonomy and evaluates the degree to which it takes advantage of the autonomy it has in setting tax rates and making other financial decisions. The paper considers some of the fiscal challenges the city now faces and some future challenges on the road ahead. It evaluates the extent to which the city can respond to these various external shocks with the resources at its disposal. It concludes that, although Toronto enjoys some local fiscal autonomy, it would benefit from having more diversified revenue sources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".