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Record W1590626192 · doi:10.15353/joci.v9i4.3142

Impact of Mobile Phones on Integration: Case of Refugees in South Africa

2013· article· en· W1590626192 on OpenAlexvenueno aff
Kevin Johnston, Kasky Bisimwa Bacishoga

Bibliographic record

VenueThe Journal of Community Informatics · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Society and Technology Trends
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeMobile phoneThematic analysisSocial integrationPoliticsQualitative researchPolitical sciencePhoneProcess (computing)Economic growthPublic relationsSociologyComputer scienceSocial scienceEconomicsTelecommunications

Abstract

fetched live from OpenAlex

This study used a qualitative approach in the form of interviews to investigate the impact of Mobile phones in enhancing the integration of refugees into South Africa. A social integration framework was used with particular attention to its three dimensions, namely the economic, social and cultural, and political participation. Thematic analysis was used to analyse the data. The study found that mobile phone usage played an important role in the social integration process of refugees. Mobile phones, contribute to a number of expected outcomes of social and economic participation, but fewer of political participation.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0140.007
Scholarly communication0.0040.002
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.034
GPT teacher head0.341
Teacher spread0.307 · 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 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

Citations15
Published2013
Admission routes1
Has abstractyes

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