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Record W2037891140 · doi:10.5539/ibr.v6n4p1

Characteristics Pertinent to the Ombudsman’s Offices: Evidence in Banks in Brazil

2013· article· en· W2037891140 on OpenAlexvenueno aff
Carlos André de Melo Alves, Cláudio Antônio Pinheiro Machado Filho

Bibliographic record

VenueInternational Business Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicBrazilian Legal Issues
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingDocumentationCorporate governanceSample (material)BusinessSubsidyContent analysisDocumentary evidencePolitical sciencePublic relationsFinanceSociologyLawComputer science

Abstract

fetched live from OpenAlex

Brazilian regulations determined from the 2nd half of 2007, the establishment of the ombudsman’s offices as an organizational component of banks giving them authority to mediate conflicts. The objective of this study is to identify the characteristics pertinent to the ombudsman’s offices of 26 banks operating in Brazil. Parallel to that it is sought to verify whether these characteristics showed significant differences in the analyzed period, from January 2008 to June 2011, and met or exceeded the regulatory forecast. A descriptive study is carried out, one that includes literature review and documentary research, analyzing the content of public documentation regarding the ombudsman’s offices. The non-probabilistic sample was based on the ‘50 Largest Banks’ report of the Central Bank of Brazil, June 2011. The analyses employed 2 items (‘Characteristics of Management’ and ‘Characteristics of Corporate Governance’) and 19 sub-items based on literature review and expert opinion on the items and sub-items. Non parametric tests were employed in the analyses. From the 494 observations on the sub-items, the main results show that the item ‘Characteristics of Management’ has 67.09% of sub-items present, and the item ‘Characteristics of Corporate Governance’ has 70.77% of sub-items present. There were no significant differences between the percentages of sub-items associated with these two items, and among the sub-items present, those which meet the regulatory forecast prevailed. The study can subsidize reflections of academics, regulators, customers, users and other stakeholders in the characterization of the ombudsman’s offices, and can help to understand the influence of regulation on the disclosure of ombudsman’s offices in banks in Brazil.

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)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.136
GPT teacher head0.451
Teacher spread0.316 · 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; both teacher heads agree on what is shown here.

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
Published2013
Admission routes1
Has abstractyes

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