Common practices in setting expenditure ceilings within national budgets
Bibliographic record
Abstract
Developing a national budget has always entailed a complex set of negotiations between national Government priorities, line ministry priorities, and a national funding envelope. This note explains how to introduce a medium term horizon into a government’s budgeting process, including the key steps involved. It provides guidance on setting aggregate and line ministry ceilings, reviewing experiences from countries with extensive experience of ceilings (for example, Finland, the Netherlands, Sweden, South Korea, Indonesia, Brazil, Australia, and Canada, among others), as well as those that have more recently adopted them. There is no one right way to set expenditure ceilings. Countries tailor expenditure ceilings to meet their specific needs, budget challenges, and capacity constraints. This note presents an iterative approach - starting from annual ceilings and gradually moving toward a medium-term expenditure framework - allowing for procedural, institutional, and organizational learning and adaptation along the way.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.120 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".