The Numbers Game: Rating the Fiscal Accountability of Canada’s Senior Governments
Bibliographic record
Abstract
Canada’s federal, provincial and territorial governments spent some $724 billion on programs and $58 billion on interest charges in 2017. They provide services ranging from defence through health and education to income supports. They have very wide taxing powers and legally unlimited authority to borrow. Canadians need to be able to monitor and influence the ways their elected representatives and government officials manage public funds. A key tool for Canadians as legislators, taxpayers and citizens to monitor and influence governments’ fiscal decisions is through their financial reports: • the budgets governments present around the beginning of the fiscal year; • the estimates legislatures vote to approve specific programs; and • the audited financial statements governments present in their public accounts after year-end. The quality and timeliness of these reports – and therefore their usefulness to legislators, taxpayers and citizens – varies widely. Our evaluation of the budgets, estimates and public accounts tabled by Canada’s senior governments in the 2017/18 fiscal year awards top marks to Alberta and New Brunswick. These A-plus provinces display the relevant numbers prominently and use appropriate and consistent accounting and aggregation in their budgets and public accounts. They also provide straightforward reconciliations of results with budget intentions, their auditors record no reservations, and their budgets and public accounts are timely. Less happily, other governments do not adhere to proper accounting standards, present budgets and estimates that are not comparable to their public accounts, bury key numbers, and are late with their budgets and/or their end-of-year results. Prince Edward Island’s D and the Northwest Territories’ D-plus put them at the bottom of the rankings. Notwithstanding some poor grades in this most recent evaluation, the financial reports of Canada’s federal, provincial and territorial governments generally improved over the years. Adherence to public sector accounting standards is better than it was, as is consistency in presentation of the key numbers. As Alberta and New Brunswick demonstrate, Canadians can get reliable, consistent and timely financial information from their governments – if they want it
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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 | 0.015 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".