Trends in Performance Budgeting in Seven OECD countries
Bibliographic record
Abstract
An international trend exists to use more information on results in public budgeting, but the focus of these initiatives varies from country to country, as demonstrated in this study of performance budgeting reforms in Australia, Canada, Sweden, the Netherlands, New Zealand, the United Kingdom, and the United States. On one hand, an evolution is taking place toward output and outcome budgeting, but on the other hand, a trend is moving toward accrual budgeting. The implementation of results-oriented budgeting evokes four major challenges: (a) to align the fiscal framework with the results-oriented budget reform, (b) to create legislative interest for performance, (c) to provide high-quality results information, and (d) to establish the leadership and authority of the central budget office. Are the results of performance budgeting worth the challenge? Very little evidence seems to exist that performance information is used in the political budgetary decision-making process or in the legislative oversight function. The major impact of results-oriented budget reform appears to be situated in the internal management of departments and agencies.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".