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Record W4416042260 · doi:10.15353/joci.v22i1.6087

ICT Use, Collective Agency and Community Transformation in Rural Bangladesh

2025· article· en· W4416042260 on OpenAlexvenueno aff
Misita Anwar, Larry Stillman

Bibliographic record

VenueThe Journal of Community Informatics · 2025
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyAgency (philosophy)Capability approachDigital divideCollectivismInclusion (mineral)Focus groupDigital inclusionPerspective (graphical)

Abstract

fetched live from OpenAlex

This paper presents a collectivist or communally-oriented interpretation of the Capability Approach (CA), within an Information and Communication Technology for Development (ICT4D) project targeted at rural communities. The paper examines how rural women in Bangladesh appropriated smartphones introduced through a digital inclusion initiative. Informed by Kleine’s Choice Framework, the study explores how agency interacts with assets and structures to shape strategic life choices. In this case, the focus is on and how these engagements show expressions of collective agency within restrictive socio-cultural environments. Findings demonstrate that while women used smartphones to access information and services, the more significant outcomes emerged through relational and collective practices. A communally-oriented perspective provides valuable insights into an ICT project's design and implementation process. It highlights the importance of recognising the community members as active participants oriented to communal, rather than solely individualistic, outcomes. This article contributes to the literature on ICT4D and Community Informatics by highlighting the need for community-centric engagement and a contextually sensitive approach in designing and implementing ICT projects in rural communities.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0030.001
Research integrity0.0000.003
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 head0.038
GPT teacher head0.278
Teacher spread0.239 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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