Rural Education and the Challenges of Using Technological Resources in Rural High Schools in the State of Ceará
Bibliographic record
Abstract
This article aims to analyze the progress of Rural Education as a public policy, access to technological development, and the use of its resources in teaching and learning in Rural High Schools located in agrarian reform settlements in the state of Ceará. It is based on documentary research data and theoretical foundations supported by Diniz (2019), Caldart (2004), Almeida (2005/2006), Santos (2016), Molina (2006), Freire (2015/2019), among others. In the first part of this work, we will focus on the historical context of Rural Education. Subsequently, we will present the concept of Rural Education and highlight the differences that exist between Rural Education and other forms of education. Emphasizing these differences is important for understanding the significant role that the rural movement plays in the protagonism of Rural Education for the rural population. In the second part, we will discuss how technological resources are integrated into Education as a pedagogy. In the third part, we will present the challenges of using technological resources in Rural Schools located in agrarian reform settlements. Considering this entire process, we can highlight that the teaching and learning processes, through the integration of technology, are part of the school curriculum, but there are limitations and lack of investments that go beyond the power of the Rural School.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".