Educational Public Policies for The Rural Area: An Analysis of the Closure of Basic Education Schools in Brazil
Bibliographic record
Abstract
Access to education for peasants in the history of Brazilian society has always been denied, with the absence of public policies for peasants, as the result of a political and social process of denial of minority groups. The text address the issue of closure of schools in the countryside as a growing social phenomenon that has been expanding in the countryside of Brazil. Thus, the general objective of this text is to analyze the issue of the right to education for peasants, focusing on the high number of school closures in the countryside, seeking to understand the consequences for the peasantry. The closure of this institutions has become a problem that effects the importance of the countryside in Brazil, given that access to education is a right for everyone, including those who live and are from the countryside. However, public management has closed activities in schools in the countryside, making it difficult the access to education for peasants. The research has a qualitative character, where initially a survey and bibliographic review was carried out supported by Magalhães (2017), Ribeiro (2012), Brasil (2010), Diniz (2010), Nogueira (2019), Silva (2018) and other authors. In a second segment, data was collected on the number of schools closed in Brazil between the years of 1997 to 2022, and finally, data and information processing and analysis thereof. The results indicate that the closure of schools in the countryside violates the right to human development of the peasantry, as it is understood that the uprooting of peasant culture, the lack of educational policies for the countryside and the cutting of spending on education are linked to the closure of these institutions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".