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Record W3196126752 · doi:10.15353/joci.v16i0.3485

Digital Justice

2020· article· en· W3196126752 on OpenAlexvenueno aff
Suguna Chundur

Bibliographic record

VenueThe Journal of Community Informatics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsDigital literacySociologyDigital divideSituatedCritical consciousnessLiteracyPedagogyEngineering ethicsKnowledge managementComputer scienceInformation and Communications TechnologyEngineeringWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

As technology use permeates many parts of society there are still groups where the penetration of technology is low: adults with little exposure to technology during their traditional learning years, users from lower SES, lower education levels, resulting in a digital divide between the digital haves and have-nots. This paper presents a community-based, mixed methods research project that endeavored to study the phenomenon of digital divide through a set of theoretical frameworks: Rawls’ principles of justice as fairness provided the overall social justice umbrella, Sen’s capability approach grounded the study in the specificities of learners’ lives and acknowledged learner diversity, and Horton’s cultural education, Freire’s critical consciousness, and Eubanks’ critical technology education provided the pedagogical lens to understand the importance of the critical learning process in digital education. The findings from the study support the concept of situated or contextual technology that seeks to increase the benefits of technology for adult learners while providing them the tools to manage complex digital environments through relatable instruction, user-centric design for technological tools and interfaces, and more robust government action in alleviating the digital divide through well-designed digital literacy programs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.080
GPT teacher head0.346
Teacher spread0.265 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2020
Admission routes1
Has abstractyes

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