Spending Reviews – a Tool to Support the Effcient Management of Public Funds
Bibliographic record
Abstract
Purpose: Respective European Union member states' interest in using spending reviews varies as there are no international mandatory regulations. The EU legislation contains general indications as to maintaining a rational fiscal policy, from the provisions of the TFUE, expanded in the Pact for Stability and Growth, and elaborated in 2011. Methodology: Adopting an interpretative research approach, this article elaborates a multiple explanatory case study design to discover how existing theories about public spending reviews are conceptualized by practitioners in their natural contexts. Findings: The deteriorated state of many countries' public finances, as a result of the global financial crisis, has increased the interest in advanced innovative consolidation and fiscal stabilization methods. Spending reviews are among the most developed and advanced methods. Such reviews were conducted both by countries that had applied this instrument before (Netherlands, Denmark, Finland, United Kingdom, Australia), and by those that introduced them for the first time (Ireland, Canada, France). However, reviews are applied in countries characterized by significant economic advancement and mature public management systems. Originality: This article analyses and draws conclusions from several selected countries' experience to date in using spending reviews. The budget functions are compared using information from the implementation of the spending reviews. This article contributes to filling two main gaps identified in the literature review.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".