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Performance budgeting practices and procedures

2016· article· en· W2397084765 on OpenAlexaboutno aff
Trevor Shaw

Bibliographic record

VenueOECD Journal on Budgeting · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Analysis and Policy
Canadian institutionsnot available
FundersGovernment of the United Kingdom
KeywordsAccountingEconomicsPerformance measurementPublic administrationPolitical scienceBusinessPublic economicsManagement

Abstract

fetched live from OpenAlex

This article examines performance budgeting practices and reforms in five OECD member countries: Canada, Ireland, the Netherlands, the United Kingdom and the United States. In each case, three dimensions of performance budgeting are analysed: performance information and monitoring, evaluation, and spending review. Reflecting upon the common experiences in each country, generalised analytical considerations are presented for practitioners planning and implementing current and future performance budgeting reforms. JEL classification: H50, H60, H61 Keywords: performance budgeting, performance-based budgeting, PBB, budget reform, performance information, Canada, Ireland, the Netherlands, the United Kingdom, the United States

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 imitation

Not 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.

metaresearch head score (Codex)0.095
metaresearch head score (Gemma)0.189
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.501

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.189
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0170.032
Science and technology studies0.0040.006
Scholarly communication0.0140.007
Open science0.0030.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.004

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.

Opus teacher head0.039
GPT teacher head0.254
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations29
Published2016
Admission routes1
Has abstractyes

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