Common practices in setting expenditure ceilings within national budgets
Bibliographic record
Abstract
Developing a national budget has always \n entailed a complex set of negotiations between national \n Government priorities, line ministry priorities, and a \n national funding envelope. This note explains how to \n introduce a medium term horizon into a government’s \n budgeting process, including the key steps involved. It \n provides guidance on setting aggregate and line ministry \n ceilings, reviewing experiences from countries with \n extensive experience of ceilings (for example, Finland, the \n Netherlands, Sweden, South Korea, Indonesia, Brazil, \n Australia, and Canada, among others), as well as those that \n have more recently adopted them. There is no one right way \n to set expenditure ceilings. Countries tailor expenditure \n ceilings to meet their specific needs, budget challenges, \n and capacity constraints. This note presents an iterative \n approach - starting from annual ceilings and gradually \n moving toward a medium-term expenditure framework - allowing \n for procedural, institutional, and organizational learning \n and adaptation along the way.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".