Bibliographic record
Abstract
Worsening performance of public finance reported by a number of countries as a result of the global financial crisis enhanced interest in advanced and innovative methods of fiscal consolidation and stabilisation. Spending reviews are amongst the most comprehensive and advanced methods of this type. In the post-2008 age, spending reviews have been carried out by countries that had used the tool in earlier periods (the Netherlands, Denmark, Finland, the UK or Australia) as well as by those who started using them for the first time (Ireland, Canada and France). Spending reviews are used in countries that are well advanced economically and whose public management systems are sufficiently mature.European Union Member States exhibit diverse interest in applying spending reviews which are not mandatory and have not been formalised in international legislation. The EU legislation contains general recommendations for the application of the rational fiscal policy enshrined first in the Treaty provisions, further developed by in the Stability and Growth Pact and detailed in 2011. The paper analyses the up-to-date experiences in using spending reviews in selected countries and draws conclusions from the process.
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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 | 0.049 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".