MétaCan
Menu
Back to cohort
Record W2787659937 · doi:10.15353/joci.v13i3.3324

An empirical study on the effects of mobile telephony usage on livelihoods in Brong Ahafo region of Ghana

2018· article· en· W2787659937 on OpenAlexvenueno aff
Stephen Bekoe, Daniel Azerikatoa Ayoung, Paul Boadu, Benjamin Yao Folitse

Bibliographic record

VenueThe Journal of Community Informatics · 2018
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsnot available
Fundersnot available
KeywordsMobile phoneLivelihoodBusinessPhoneCitizen journalismRelevance (law)Mobile telephonyFocus groupInternet privacyMarketingTelecommunicationsComputer scienceGeographyPolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Meaningful use of mobile telephony can enhance human development and capabilities thereby empowering people to lead lives they value. They are enabling technologies to deliver human-centred development. This article explores the effects of mobile phone use on livelihoods of users in eight districts in Brong Ahafo region of Ghana. A mixed method approach was employed and qualitative research was used as a dominant paradigm. Interview questionnaires, focus group discussions and observation were used. The study showed that mobile phone ownership was high and their uses were characterised by greater uniformity across socio-economic groups and gender. Mobile phones enhanced traditional structures, facilitated business links, and face-to-face interactions as well as strengthening community ties. Users acknowledged the impact of mobile phones in their ability to deal with family emergencies. Poor network connectivity and power outages were major obstacles to mobile phone usage. The study makes original contributions to the knowledge of practical relevance in the ICT4D field as well as with respect to these under-researched Ghanaian regions and provides evidence for policy formulation to improve quality of services in Ghana and elsewhere. The participatory Field Research also provided space for in-depth engagement with local people to understand the technology in social and development contexts.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.290
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0050.001
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 head0.046
GPT teacher head0.326
Teacher spread0.280 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Community InformaticsSame topicICT in Developing CommunitiesFrench-language works237,207