Analysis of the Implementation of the Federal Government's Sports Incentive Law: A Look at Proponents
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".