An empirical study on the effects of mobile telephony usage on livelihoods in Brong Ahafo region of Ghana
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".