Potential of Bluetooth Wireless Technology as a Tool for Agricultural Extension
Bibliographic record
Abstract
Information and Communication Technology (ICT) provides opportunities to improve farmers’ livelihoods. Bluetooth wireless technology (BWT) is a simple cellphone-based innovation that can significantly reduce the cost of disseminating information to farmers. A two-stage survey was conducted in Ghana in 2011 and 2012 to: (i) appraise cellphone ownership, cost, and the existence of BWT, and (ii) assess the potential of using BWT to disseminate information to farmers on grain storage technology. The survey was conducted in four districts in the Northern and Ashanti regions of Ghana. Results of the appraisal study show that there were 27 cellphones for about 1,000 people, with 20% owned by women. The majority (78%) of phones were purchased new for $20 or less. About half (48%) of the cellphones had BWT. The follow-up study showed that most respondents learned (91.7%) and received (88.3%) the videos on grain storage (Purdue Improved Crop Storage-PICS) technology via BWT from extension agents. Three-fourths of the respondents watched the PICS videos on their cellphones six times or more. Among those who received the PICS videos, each viewed and shared it with nine and seven more people, respectively. The overall results support the effective use of BWT embedded in basic phones as a tool for conveying agricultural extension messages to farmers. Development partners and extension services should take advantage of BTW embedded in basic phones to improve access to agricultural and health information.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".