Awareness of ICT-Based Projects and the Intensity of Use of Mobile Phones Among Smallholder Farmers in Uganda
Bibliographic record
Abstract
As the use of information and communication technologies (ICT) is embraced in Uganda, determinants of awareness of ICT based projects remain unknown. The intensity of use of mobile phones among smallholder farmers in the areas where such projects operate is unclear. To address this knowledge gap, 346 smallholder farmers in two ICT project sites in Mayuge and Apac districts were subjected to econometric analysis using bi-variate logistic and zero-inflated negative binomial regression models to ascertain determinants of projects’ awareness and intensity of use of mobile phones. The authors find that education, distance to input markets, and membership in a group positively influence awareness. The decision to use a mobile phone for agricultural purposes is affected by distance to electricity and land cultivated and negatively influenced by being a member of any farmer group. Lastly, intensity of mobile phone use is affected by age, farming as the major occupation, and distance to an internet facility, being a member of a project, having participated in an agricultural project before, value of assets, size of land cultivated, possession of a mobile phone, and proximity to agricultural offices. The paper discusses policy implications of these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".