Bibliographic record
Abstract
Canada’s governments have regularly missed budget spending and revenue targets during the last decade. Taken together, the spending overruns of federal, provincial and territorial governments have surpassed $53 billion in the last 10 years. If budget targets were met more accurately, current debt loads, tax burdens and deficits would be lower and more manageable. There is considerable variation in the financial reporting of Canadian governments. Some jurisdictions present budget and public accounts figures clearly, making the results in both documents easily comparable. Further, some governments table their public accounts in a timely way, with clean audits and updates on changes to budget plans during the fiscal year. Others, however, do not. The federal government and the governments of Ontario and New Brunswick are leading the way in presenting clear public accounts documents and making an effort to compare and explain deviations from budgeted and year-end revenue and spending figures. At the other end of the scale are the governments of Quebec, Nova Scotia, Prince Edward Island, Newfoundland and Labrador, and the Northwest Territory and Nunavut, which do not present straightforward and comparable figures in their budgets or public accounts. This fifth annual study of governments’ fiscal accountability measures each jurisdiction’s 10-year fiscal record for bias (the average difference between budget projections and actual results) and accuracy (over-shoots and under-shoots of budget targets). The results for spending show Ottawa having the best (lowest) bias score with Newfoundland and Labrador, Ontario and Nova Scotia not far behind. On accuracy, Quebec, New Brunswick, Ontario and Nova Scotia show respectable scores. Resource-dependent jurisdictions, such as Saskatchewan and Alberta, have poor bias and accuracy scores, making them more likely to miss budgeted spending promises than other jurisdictions. The past decade’s cumulative revenue and spending overshoots, and the emerging understanding that a lack of fiscal transparency undermines good management of public money, should inspire Canadian senior governments to improve their financial reporting and their adherence to targets – and for legislators and voters to hold them more closely to account.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".