Mobile phones and social structures: an exploration of a closed user group in rural Ghana
Bibliographic record
Abstract
BACKGROUND: In the Millennium Villages Project site of Bonsaaso, Ghana, the Health Team is using a mobile phone closed user group to place calls amongst one another at no cost. METHODS: In order to determine the utilization and acceptability of the closed user group amongst users, social network analysis and qualitative methods were used. Key informants were identified and interviewed. The key informants also kept prospective call journals. Billing statements and de-identified call data from the closed user group were used to generate data for analyzing the social structure revealed by the network traffic. RESULTS: The majority of communication within the closed user group was personal and not for professional purposes. The members of the CUG felt that the group improved their efficiency at work. CONCLUSIONS: The methods used present an interesting way to investigate the social structure surrounding communication via mobile phones. In addition, the benefits identified from the exploration of this closed user group make a case for supporting mobile phone closed user groups amongst professional groups.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".