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Record W1484589679 · doi:10.5539/ibr.v8n8p181

Control and Public Management Performance in Brazil: Challenges for Coordination

2015· article· en· W1484589679 on OpenAlexvenueno aff
Cecília Olivieri, María Rita Loureiro, Marco Antônio Carvalho Teixeira, Fernando Luiz Abrucio

Bibliographic record

VenueInternational Business Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Public Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPrinciple of legalityAuditBusinessControl (management)Diversity (politics)Management control systemPublic administrationProcess managementGovernment (linguistics)Public relationsAccountingPolitical scienceLawManagementEconomics

Abstract

fetched live from OpenAlex

This study analyzes the multiplicity of organs and control actions as a distinctive characteristic of the Brazilian control system, and the positive effect of the performance of these organs upon the management of federal programs. As shown in international literature, we can also see in Brazil an expansion of the action of control organs, which have started evaluating performance and, therefore, go beyond mere verification of the legality of administrative actions. Nevertheless, the multiplicity and diversity of these organs have meant the appearance of tensions and conflicts in the relationship between controllers and those controlled. Through the analysis of documents and interviews with managers and the controllers of federal government oversight bodies, we conclude that the process of auditing and evaluation leads to the appearance of cooperative practices that improve the administration’s performance, but which themselves need improving.

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)0.024
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0060.013
Scholarly communication0.0120.006
Open science0.0020.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.224
GPT teacher head0.486
Teacher spread0.262 · 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 designNot applicable
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

Citations16
Published2015
Admission routes1
Has abstractyes

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