Perceptions of ICT use in rural Brazil: Factors that impact appropriation among marginalized communities
Bibliographic record
Abstract
This study of digital inclusion among the rural poor examines how residents of remote mountain communities in Brazil perceive the use of information and communication technologies (ICTs). It analyzes social factors that impact ICT appropriation and the behaviors and attitudes that advance digital literacy among marginalized rural populations. The authors conducted factor analysis and logistic regressions to survey data collected. Results confirm the presence of a gender divide in ICT adoption. Women were more likely to perceive that ICT use brings social benefits to the community, and considered that ICTs provide better opportunities for the young.Este estudio sobre inclusión digital entre los pobres en comunidades rurales examina cómo es que los residentes de comunidades serranas remotas en Brasil perciben el uso de la las tecnologías de la información y comunicación (TICs). Analiza los factores sociales que impactan la apropiación de TIC, así como el comportamiento y las actitudes que permiten el avance de la alfabetización digital en poblaciones rurales marginalizadas. Los autores realizaron un análisis factorial y regresiones logísticas de la información recolectada a través de encuestas. Los resultados confirman la presencia de una brecha de género en la adopción de TIC. Las mujeres son más propensas a percibir que el uso de ICT trae beneficios sociales para la comunidad, y también consideraron que las TICs proporcionan mejores oportunidades para los jóvenes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".