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Record W1597468459 · doi:10.15353/joci.v7i1-2.2566

Evaluating ICT Adoption in Rural Brazil: A Quantitative Analysis of Telecenters as Agents of Social Change

2011· article· en· W1597468459 on OpenAlexvenueno aff
Paola Prado, Mauro Araújo Câmara, Marco Aurélio Figueiredo

Bibliographic record

VenueThe Journal of Community Informatics · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyEntertainmentOddsDigital literacyICTSLogistic regressionLiteracyDigital divideBusinessPublic relationsEconomic growthSociologyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

This quantitative study surveyed 538 adults in isolated rural settings in the state of Minas Gerais, Brazil, in order to examine whether telecenters operated by the non-profit organization Gems of the Earth in the area improve digital literacy and promote social change in those remote mountain communities. Using multivariate logistic regression, the study examined how individuals use information and communication technologies (ICTs) at the telecenter, and tested for predictors of their use. The findings confirm that these rural communities use ICTs for entertainment, to engage in civic participation, and to practice professional skills. Results also indicate greater odds of ICT use among individuals aged 18 to 24 and among those who seek diversion. The findings suggest that digital inclusion impacts these isolated communities by creating opportunities for entertainment, civic engagement, professional development, and education in ways that may positively impact human development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.298
GPT teacher head0.470
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2011
Admission routes1
Has abstractyes

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