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Record W4415043949 · doi:10.1093/bjsw/bcaf215

Information and communication technology in transnational families: Understanding the experiences of children left behind in urban Ethiopia

2025· article· en· W4415043949 on OpenAlexaff
Eliyas Taha Aliye, Faye Mishna, Ashenafi Hagos Baynesagn

Bibliographic record

VenueThe British Journal of Social Work · 2025
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsUniversity of Toronto
FundersAddis Ababa University
KeywordsInformation and Communications TechnologyThematic analysisGlobeContext (archaeology)Qualitative researchWork (physics)Digital divideContent analysisDescriptive statistics

Abstract

fetched live from OpenAlex

Abstract Despite the tremendous flow of international migration, Ethiopia has not been given due consideration in the literature on transnational families. The current study explored the perspectives of children using information and communication technology (ICT) with their migrant parent(s). A descriptive qualitative approach, which provides rich descriptive content from participants’ perspectives, was employed. We conducted interviews with twenty-five participants from Adama and Addis Ababa. A thematic analysis approach was used to analyse the data. Based on the analysis of the interviews, five overlapping themes were revealed: mixed emotional responses to ICT communication, transformation of children’s roles, distance parenting, shifts in contact, and emotional detachment. This study contributes by expanding our understanding of children left behind in Ethiopia and identifying the need for tailored social work services. Equitable digital access in the Ethiopian national child policy is found to be essential, which can be demonstrated by investing in school-based Wi-Fi hubs to facilitate parent–child communication and attachment. By highlighting children’s right to equitable digital access to communication with their migrant parents, this study contributes to child welfare policy across the globe in the context of migration.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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