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Record W2886261833

Public Sector Budget Reforms: Trends and Challenges

2004· article· fr· W2886261833 on OpenAlexaboutno aff
Miekatrien Sterck, Bram Scheers, Geert Bouckaert

Bibliographic record

VenueRevue internationale de politique comparée · 2004
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
Fundersnot available
KeywordsBudget processAccountingCashPublic sectorEconomicsProcess (computing)International comparisonsBusinessPublic economicsFinancePolitical scienceEconomic growthEconomyPoliticsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Public budgeting systems have been modernized in recent decades. The increased need for performance, efficiency, and the presentation of accounts have given rise to a change in approach, which is reflected in the shift from a budget expressed in terms of expenditure items on a modified cash basis to an approach focusing on impacts, performance, and costs. In spite of this general trend, result-oriented budgeting has been applied in different ways and to varying extents depending on the country. Some countries have fully implemented annual budgeting and accounting practices that focus on impacts, or are in the process of doing so (Australia, the United Kingdom, and Sweden) or performance budgeting (New Zealand). Then there are countries that have adopted impact-oriented budgeting but have maintained modified cash accounting (the Netherlands). Finally, other countries have retained a traditional modified cash program budget where performance and results are only included in the explanatory documents associated with the budget process (Canada and the United States). Governments are thus actively striving to reform their budget systems, although they have to deal with a number of challenges, since their budget systems serve multiple purposes and users that may not always be compatible.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.246
Teacher spread0.190 · 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 designTheoretical or conceptual
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

Citations0
Published2004
Admission routes1
Has abstractyes

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