Review of International Practices for Determining Medium-Term Resource Needs of Spending Agencies
Bibliographic record
Abstract
This paper reviews international practices for ‘bottom-up costing’ for medium-term expenditure frameworks. Medium-term expenditure frameworks are important because they incorporate the multi-annual nature of the fiscal policy into the budget process, mitigating its short-term bias. They also allow for the incorporation of the effects of policy decisions and provide for a comprehensive fiscal sustainability picture. However, there are significant gaps in current understanding of how costing and cost information is implemented within medium-term expenditure frameworks. The objective of this paper is to assemble information on practices used in Australia, Austria, Canada, and the Netherlands to determine program costs as part of medium-term expenditure planning, and to provide preliminary observations on the strengths and weaknesses of current arrangements. The overall findings are that current costing practices fall short of the declared objectives of medium-term expenditure frameworks. The report makes some specific observations on the status of costing practices within the surveyed jurisdictions, namely that: (i) although there is no typical medium-term expenditure frameworks, some features tend to be more compatible with a greater role for bottom-up costing; (ii) where costing practices are specified, they are generally expected to be used across the entire budget, but in practice the focus is on new or expanded programs; (iii) the capacity to distinguish existing and new programs is important in utilizing cost information; (iv) the distinction between conventional program costing and forecasting helps to explain differences in costing approaches; and (v) where they are specified, costing methodologies are recommended but not mandated.
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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 | 0.029 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".