Bibliographic record
Abstract
The purpose of involving the legislative branch in the budget process is to promote good governance, control the executive’s power. and improve the efficiency of fiscal management. The legislative arm should work to approve the budget, laws, and oversee the executive. The requirement for legislative approval of financial measures is a democratic foundation stone that is enshrined in Constitutions around the world. Though the exact role and capacity may differ, each legislature has budgetary powers from the Organic budget law or Constitution. While case studies have been conducted on budget institutions, few have included quantitative measures or compared legislative budget institutions. This study constructs a legislative budget institution index, using the 2007ㆍ2008 OECD and World Bank survey of budget practices and procedures database, for the purpose of cross-national comparison and explaining differences. The index, constructed using the methods of Wehner and Alesina et al., arranges 48 countries according to two-sub indices: financial power and organization capacity. The results reveal the United States, with its presidential system, to have the highest scores. Sweden, which has experience of budgetary and financial reform has the second highest score. On the other hand, England, Ireland, and Canada, which operate under the Westminster-system have low scores. Government system is found to have a significant impact on the legislative budget institution index, whether OECD membership is controlled for or not. Similarly, the financial power index is found to be significant, but the organization capacity index has less impact.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".