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Record W2264541561 · doi:10.20355/c5c013

The Construction of Full Citizenship in a Vision of the Brazilian Digital Inclusion in Education

2014· article· en· W2264541561 on OpenAlexvenueno aff
Francisco Robert Ferreira Dos Santos, Ildefonso Rodrigues Teixera

Bibliographic record

VenueJournal of Contemporary Issues in Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Political Issues
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipInclusion (mineral)CyberspacePublic sphereSociologyPoliticsSpace (punctuation)Public spaceSocial transformationState (computer science)Work (physics)The InternetPolitical sciencePublic relationsSocial changeSocial scienceEngineeringComputer scienceLaw

Abstract

fetched live from OpenAlex

This article discusses the importance of digital inclusion in the construction of citizenship. The central point when discussing social inequality is citizenship with impacts on the structure transformation present in society. The opportunities created by digital inclusion can transform the conditions of the individual, leading to the construction of citizenship from greater participation in political life and in public decisions. Digital inclusion can promote the participation of the individual in cyberspace which becomes the sphere for public debates and a space for State decisions. The critical use of techniques and information technology along with other actions which promotes equality can lead to the development of full citizenship and requires a new transformation in the meanings of work, responsible for occupation and social inclusion. Considering the adjustments for digital inclusion, social programs can go "beyond mere access" to computers and internet and changes and in the social space.

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)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0100.029
Scholarly communication0.0090.007
Open science0.0010.009
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.352
Teacher spread0.339 · 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 designNot applicable
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

Citations0
Published2014
Admission routes1
Has abstractyes

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