Fuzzy Finances: Grading the Financial Reports of Canada’s Municipalities
Bibliographic record
Abstract
In nearly all larger Canadian municipalities, obscure financial reports – notably inconsistent presentations of key numbers in budgets and end-of-year financial statements – hamper city councillors, 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 does what it plans to spend this year compare to what it spent last year?” are hard or impossible for a non-expert citizen or councillor to answer. The differences between budget accounting methods and presentations of financial results have realworld consequences. For example, by presenting net rather than gross budget figures, municipalities obscure key activities and understate both their revenue and spending. By using cash rather than accrual accounting, they exaggerate infrastructure investment costs, hide the cost of pension obligations and make it hard to match the costs and benefits of municipal activities. Moreover, many municipalities approve their budgets after significant money has already been committed or spent in the fiscal year, do not publish their financial results in a timely way, and bury key numbers deep in their statements. This report card grades the financial presentations of major Canadian municipalities in their most recent budgets and financial statements. Calgary registered the largest year-over-year decline in budget clarity: like Durham Region, it provides little information in reader-friendly form. More happily, Vancouver, Surrey, B.C., and Peel and Niagara Regions in Ontario garnered the highest marks for clarity of financial presentation. Our key recommendations are: (1) that municipal governments should present their annual budgets on the same accounting basis as their year-end financial statements and (2) 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. For their part, provincial governments that impede the use of accrual-based budgets – by mandating that cities present separate operating and capital budgets, for example – should stop doing so. Indeed, provinces should mandate cities to present accrual budgets so the fiscal picture of municipalities and the province use the same transparent standard. Even in cases where a province is an impediment, municipalities could 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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.032 | 0.031 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".