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Record W4378715991 · doi:10.5539/ijef.v15n6p66

IPSAS: Which Stage Is the Brazilian Public Sector Accounting In?

2023· article· en· W4378715991 on OpenAlexvenueno aff
Abimael de Jesus Barros Costa

Bibliographic record

VenueInternational Journal of Economics and Finance · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic Research in Diverse Fields
Canadian institutionsnot available
FundersFundação de Apoio à Pesquisa do Distrito FederalConselho Nacional de Desenvolvimento Científico e TecnológicoUniversitat de València
KeywordsAccountingPublic sectorPublic accountingGovernment (linguistics)Latin AmericansVisibilityEconomicsBusinessPolitical scienceLawEconomyGeographyAudit

Abstract

fetched live from OpenAlex

This article aims to contribute to the visibility of the development process of Government Accounting systems in Latin America, especially Brazil’s case. Based on analysis of the period between 2008 and 2018, it concludes that Law 4,320/1964 needs to be updated; the Brazilian Public Sector Technical Accounting Standards (NBC TSP) represent the IPSAS in Brazil (translated and adjusted to local reality); the implementation of the Full IPSAS was applied to all Brazilian federation entities (Central Government, 26 States, Federal District and 5,570 Municipalities); the Handbook of Accounting Applied to the Public Sector (MCASP) is the main IPSAS guide; and, finally, the greatest difficulties faced by public accountants and managers are the lack of public workers, technology and training. Among the 60 master’s dissertations carried out by the PPGCCs focusing on the area of public accounting, only 14% addressed topics related to public accounting.

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)0.008
metaresearch head score (Gemma)0.016
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 score0.110
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.002
Scholarly communication0.0080.007
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.061
GPT teacher head0.343
Teacher spread0.282 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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