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Record W1566185481 · doi:10.1596/1813-9450-7289

Review of International Practices for Determining Medium-Term Resource Needs of Spending Agencies

2015· book· en· W1566185481 on OpenAlexaboutno aff
Michael Di Francesco, Rafael Barroso

Bibliographic record

VenueWorld Bank, Washington, DC eBooks · 2015
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsTerm (time)Medium termResource (disambiguation)BusinessComputer scienceEconomicsMacroeconomicsPhysics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.095
GPT teacher head0.292
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations4
Published2015
Admission routes1
Has abstractyes

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