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Analysis of the Implementation of the Federal Government's Sports Incentive Law: A Look at Proponents

2021· article· en· W4210260662 on OpenAlexaff
Marcus Peixoto de Oliveira, Rafael Silva Diniz, Luciano Pereira da Silva

Bibliographic record

VenueRevista Intercontinental de Gestão Desportiva · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPhysical Education and Sports Studies
Canadian institutionsImpact
Fundersnot available
KeywordsExecutorIncentiveGovernment (linguistics)Public domainPrivate sectorPublic administrationState (computer science)Public relationsPublic sectorPublic policyPolitical scienceSociologyLawEconomicsComputer science

Abstract

fetched live from OpenAlex

This article aims to identify the legal nature of proponents institutions who participate in the mechanism of the Sports Incentive Law (LIE), Law n⁰ 11.438/2006, confirming or refuting the hypothesis of absolute majority representing the considered Third Sector organizations and, from this, understanding the relationship of this implementing actor, non-state, with the Government, in the light of the so-called fourth generation of public policy implementation studies. For this purpose, the documentary research method with secondary data collection from the Special Sports Secretariat was used through a quantitative approach, as well as a qualitative approach through the analysis of semi-structured interviews with managers of the proposing institutions. Theoretical basis for understanding and analyzing the implementation of the LIE was given by the light of the fourth generation of public policy implementation studies characterized by multiple models. As results, was confirmed the domain of the Third Sector as the main executor of the projects, ahead of other private associations and public institutions, pointing to the need for specific attention from the Brazilian Government to these actors, in the policy implementation. In this sense, the possibility of an analytical framework based on the new institutional arrangements is ratified.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.311
Teacher spread0.301 · 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 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
Published2021
Admission routes1
Has abstractyes

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Same venueRevista Intercontinental de Gestão DesportivaSame topicPhysical Education and Sports StudiesFrench-language works237,207