Radio+ and Open Source Software for Mobilizing Knowledge for Sustainable Agriculture in Sri Lanka
Bibliographic record
Abstract
Information and Communications Technologies (ICTs) considered an important platform towards sustainable agriculture, specifically when mobile and Free and Open Source (FOSS) applications are used to broadcast radio referred to here as Radio+. Radio+ plays an important role in educating and entertaining farmers and give beneficial farming information. Radio+ can encourage younger farmers to increase their participation in the agricultural sector and motivate them to use new ideas and techniques for agriculture. In order to boost sustainable agricultural systems, Free and Open Source (FOSS) applications recognized for low cost and user-friendly platform used on mobile devices such as Smart Phones. In Sri Lanka, partners including the Department of Export Agriculture, Wayamba University and LIRNEasia collaborating with researchers at the University of Alberta and University of Guelph have created a community of practice for farmer media convergence. A partnership development grant from the Social Sciences and Humanities Research Council of Canada (SSHRC) in 2012 enabled the partners to establish a community-university research partnership in order to explore the potential for low cost ICTs to enhance knowledge mobilization practices within agricultural communities of practice in Sri Lanka. This paper outlines the experiences of the initiative and its use of Open Source (FOSS) and radio+ for sustainable agriculture in Sri Lanka.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".