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Record W4388510124 · doi:10.5539/ijef.v15n12p16

Fiscal Pedal: Fraud or Creative Accounting? A Theoretical Understanding of the Dangerous Fiscal Cycles that Affected the Brazilian Economy

2023· article· en· W4388510124 on OpenAlexvenueno aff
Jeremias Pereira da Silva Arraes, José Matias-Pereira, João Abreu Faria Bilhim

Bibliographic record

VenueInternational Journal of Economics and Finance · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic Research in Diverse Fields
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingImpeachmentPhenomenonCreative accountingAuditCommissionGovernment (linguistics)EconomicsAccounting information systemPolitical scienceLawPoliticsFinance

Abstract

fetched live from OpenAlex

This essay aims to discuss, in a theoretical way, how creative accounting is presented in Brazil in the years 2014 and 2015, which resulted in an impeachment process and affected the Brazilian economy. The methodology is qualitative with the application of content and document analysis techniques. The basis of the accounting information is taken from the analyzes of the special impeachment commission and the Federal Court of Auditors in relation to the accusation of a crime of fiscal responsibility to the detriment of the president of the republic. The research discusses the accounting operations carried out by the government and which resulted in the famous expression “fiscal pedaling”. In light of accounting theory Hendriksen and Van Breda (2018), there is a complexity in the analysis that has led to frequent divergences regarding the objectives of accounting and the nature of the economic environment in which it operates. Thus, as research results, it is possible to observe that the concepts given to creative accounting and fraud are close, but they are not similar, where characteristics such as intentional error and the transit of accounting operations, outside the accounting regulations and principles, end up elucidating the particularities of each phenomenon.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.062
GPT teacher head0.342
Teacher spread0.280 · 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 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
Published2023
Admission routes1
Has abstractyes

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