Avaliação dos impactos do Programa Sua Nota Vale Dinheiro na melhoria das atividades das instituições beneficiadas com o programa
Bibliographic record
Abstract
This text consists in presentation of final paper for Master´s Degree in “Public Policies Evaluation” at Universidade Federal do Ceará – UFC. The research is focused on the subject of improvement in activities referred to philanthropic institutions registered in the program called “Sua Nota Vale Dinheiro” (PSNVD), implemented by the State Department of Finance – SEFAZ/Ceará/Brasil. The main objective of the program is concerned with the issuance of tax documents to support financial resources for the state, by collecting taxes over operations relating to the Circulation of Goods and Services for Interstate and Intercity Transportation and communication (ICMS), to cover the services needed for the well-being of all citizens such as education, health care, transport and housing. The study shall examine if the program (PCNVD) has provided improvements for benefited institutions and for people from the institutions. In general, the following questions are presented in basic issues: a) Has the program brought significant improvements to registered institutions? b) Has the program contributed for the awareness of the population about social function of tax or is it only a policy that has a bias of a revenue collection tax? It was adopted a methodology involving both a quantitative as well as qualitative approach. Opened interviews were held to get a greater number of information about the institutions and main achievements with the program resources. The research was complemented with other information about those who have been involved with the program results and also about the total collection of taxes in the state. Questionnaires were applied with direct and closed questions to complement data collection being realized a documentary and bibliographical research on reports published in newspapers and magazines to consolidate knowledge.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".