Information and communication technology in transnational families: Understanding the experiences of children left behind in urban Ethiopia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".