Credibility on the (Bottom) Line: The Fiscal Accountability of Canada's Senior Governments, 2013
Bibliographic record
Abstract
Each spring, Canada’s federal, provincial and territorial legislatures vote budgets that set out their spending and revenue goals for the fiscal year. Budget votes are critical for holding governments accountable to legislators, and in turn to voters and taxpayers. Over the last decade, however, Canada’s senior governments have overshot their spending targets by some $47 billion combined. More accuracy in hitting budgeted amounts would have made today’s taxes and public debt lower. A related problem is deficiencies in financial reporting. In many provinces and territories, the average citizen or legislator would have trouble simply finding and comparing the key numbers in the budget and in the end-of-year financial reports. While Ottawa and Ontario prepare their principal financial documents using the same basis of accounting, display the relevant numbers prominently, and provide informative reconciliations between budgets and results, in most of the other provinces and territories, inconsistent presentations of multiple revenue and spending figures would stump any but the most expert reader. Our 2013 fiscal accountability survey of Canada’s senior governments’ evaluates the clarity and adherence to public sector accounting standards in each government’s budgets and public accounts, and assigns letter grades to each one. It also shows the results of a straightforward attempt to overcome varied financial presentations across the country by comparing budgeted to actual changes in spending and revenue. This exercise shows a substantial overshoot of spending – and an even larger overshoot of projected revenues – over the past decade. Expressed as percentages of their budgeted expenditures in the current fiscal year to allow comparisons, Alberta and Saskatchewan showed the biggest overshoots among the provinces – averaging about 4 and 5 percent – over the period, while Yukon and Nunavut were even worse. The news is not all bad, however. Over the most recent five years, most of Canada’s senior governments came closer to both their spending and revenue targets than they had during the previous five years. Our survey concludes with several suggestions about how Canada’s senior governments can improve their financial reporting, and with it the ability of legislators and voters to hold them to account for hitting their budget targets.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".