Two Sets of Books at City Hall? Grading the Financial Reports of Canada’s Cities
Bibliographic record
Abstract
In nearly all Canada’s larger cities, obscure financial reports – notably inconsistent presentations of key numbers in budgets and end-of-year financial reports – hamper legislators, ratepayers and voters seeking to hold their municipal governments to account. Simple questions like “How much does your municipal government plan to spend this year?” or “How much did it spend last year?” are hard or impossible for a non-expert citizen or councilor to answer. By presenting net rather than gross figures in their budgets, most cities understate their revenue and spending and obscure key activities. The differences in accounting methods in most cities’ budgets as compared to the presentations of their financial results are large, and have real-world consequences. By using cash rather than accrual accounting, cities exaggerate the costs of investments in infrastructure, hide the cost of pension obligations, and make it hard to match the costs and benefits of municipal activities to taxpayers and citizens. Moreover, many cities present their budgets late, after significant money has already been committed or spent, do not publish their financial results in a timely way, and bury key numbers deep in their reports. This report grades the financial presentations of major cities in Canada in their most recent budgets and financial reports. Toronto and Winnipeg are at the bottom, with Fs. In addition to approving their budgets many weeks after their fiscal years had started, these cities provide little information in reader-friendly form. More happily, the cities of Brampton, Calgary, Halifax, Halton and Vancouver garner higher marks. Our key recommendations are that municipal governments should present their annual budgets on the same accounting basis as their financial statements, and that budgets should show gross, not net, revenue and spending figures. Budgets should use accrual accounting, recording revenues and expenses as the relevant activities occur over time. Provincial governments exercise decisive control over cities, so those that impede accrual-based budgets at the municipal level should stop doing so. Now is an opportune time for this change: major reviews of the acts governing municipalities are nearly complete in Alberta and Ontario. Even in cases where a province is the impediment, cities can release the relevant information on their own – and they should.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.172 | 0.060 |
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".