Facebook for digital agricultural extension services: The case of rooftop gardeners in Bangladesh
Bibliographic record
Abstract
The use of social media platforms has revolutionized the way rural farmers receive information and support from agricultural experts and extension services. However, the role of social media in meeting the information and technological needs of urban rooftop gardeners remains unclear. The study utilizes the Theory of Virtual Community Practice (VCoP) to address this research gap and employs a qualitative, inductive research approach. We investigate the potential of Facebook groups in bridging the extension service gap and addressing the information deficit faced by urban rooftop gardeners. Our study involves extracting data from two Facebook-based VCoPs using CrowdTangle and analysis using Atlas-ti software. The findings of our study highlight six communication behaviors: supporting outreach, crowdsourcing, knowledge sharing and learning, engaging groups or communities, cooperating, and popularity and promotion. These behaviors provide insights into various aspects of community engagement, interaction, and outcomes. With the help of social media platforms, rooftop gardeners can connect, share experiences, seek advice, and access valuable information on rooftop gardening. This study is the first to explore the potential of Facebook groups in bridging the extension service gap and addressing the information deficit faced by urban rooftop gardeners.
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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 | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".