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Adoption of the budget by competence in Brazil based on comparative experiences in the USA, Canada, United Kingdom, Australia, and New Zealand

2025· article· en· W4406052615 on OpenAlexaboutno aff
Arianne Peruzo Pires Gonçalves, Roberto Sérgio do Nascimento, Ricardo Viotto

Bibliographic record

VenueREVISTA AMBIENTE CONTÁBIL - Universidade Federal do Rio Grande do Norte - ISSN 2176-9036 · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicBusiness and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityCompetence (human resources)AccountingEnvironmental accountingSustainabilityAccrualValuation (finance)NegotiationPoliticsNormativePolitical scienceEconomicsBusinessEarningsManagement

Abstract

fetched live from OpenAlex

Purpose: To analyze the feasibility of adopting competency budgeting in Brazil. Methodology: Qualitative, exploratory, bibliographic, documental, and content analysis nature, with the cataloguing of the material based on environmental attributes (political commitment, human capital, long-term sustainability, and macroeconomic policy) and technical attributes (depreciation and valuation of assets, capital expenditure, symmetry between reports, recognition of benefits and accountability). The work is comparative in nature based on the practices adopted in the USA, Canada, United Kingdom, Australia, and New Zealand. Results: The examinations of environmental attributes in comparison with technical attributes were more homogeneous in reference to the countries analysed. It was identified that the technical attributes were the ones that were more casuistic, aiming to meet the specific situations of the participating countries. In Brazil, if the reform were implemented, the study pointed to attributes related to macroeconomic (environmental) policy and accountability (technical) as being the most favorable. Contributions of the Study: It was identified that the most advanced experiences on accrual budgeting come from countries of Anglo-Saxon origin, whose accounting systems are convergent with those used by companies. In this sense, it is hoped that the work can contribute to the broadening of the discussion of the systematics, in view of the indication of environmental and technical attributes considered to facilitate and/or hinder the process.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.075
GPT teacher head0.335
Teacher spread0.261 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueREVISTA AMBIENTE CONTÁBIL - Universidade Federal do Rio Grande do Norte - ISSN 2176-9036Same topicBusiness and Management StudiesFrench-language works237,207