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Record W4206458159 · doi:10.5430/ijba.v13n1p1

Transparency in the Municipal Public Management: An Evaluation of Rio Grande do Sul State’s Municipalities

2022· article· en· W4206458159 on OpenAlexvenueno aff
Andressa Petry Müller, Nelson Guilherme Machado Pinto

Bibliographic record

VenueInternational Journal of Business Administration · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic Research in Diverse Fields
Canadian institutionsnot available
Fundersnot available
KeywordsTransparency (behavior)State (computer science)Index (typography)BusinessDescriptive statisticsAccountingPublic administrationComputer scienceStatisticsPolitical scienceComputer securityMathematics

Abstract

fetched live from OpenAlex

The objective of the present study is to determine the transparency level of Rio Grande do Sul state’s municipalities, identifying the variables that explain it. This is a documental and quantitative research, observing the most recent information disclosed in the state’s transparency websites, identifying if the data disclosure is correct, through the Municipal Public Management’s Transparency Index, as well as the regression analysis and descriptive statistics. In this way, it is possible to observe that most state’s municipalities present an average level of transparency. Besides, it was observed that, from the eleven socioeconomical variables that were analyzed, only five are capable of influencing the transparency index. Therefore, many measures must still be adopted by the state’s municipal managements, so that they enforce the Access to Information Law and fulfill all of the aspects that come from the transparency.

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)0.019
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.173
GPT teacher head0.431
Teacher spread0.257 · 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
Published2022
Admission routes1
Has abstractyes

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