How does misinformation influence the digital agri-food advisory service? Multi-stakeholder Perspectives from Sri Lanka
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Misinformation can be a significant issue in the agri-food sector, just as it is in health and politics. The rise of social media has changed the way agri-food actors communicate, making it easier for them to connect and overcome the challenges of limited resources and slow service. However, while social media’s ease of access and rapid dissemination of information has benefits, it also creates a fertile ground for spreading misinformation. Understanding misinformation and its related issues can help inform strategies to address it. This study analyzed the perspectives of farmers, researchers, advisors, and input dealers on misinformation and its influence on agri-food advisory services in the virtual realms of social and online media in Sri Lanka. Using Q-methodology, we found three distinct perspectives on the issue. The first perspective sees social media as a great tool for connecting people but also a significant source of misinformation. The second perspective shows that the main motivation for spreading misinformation in the agri-food sector is the profit generated by those unfamiliar with farmers’ challenges. The third perspective sees misinformation as spreading quickly and difficult to counteract but acknowledges that it can be posted and shared by mistake. All three perspectives emphasize the need to improve digital literacy skills, develop an effective moderation strategy, and adopt a political economy of agri-food misinformation, and cultivate a multi-stakeholder collaboration to combat it. This study is the first of its kind and helps improve our understanding of this pressing issue. It provides valuable insights and impetus for future research, as we anticipate that agricultural advisory services will continue to embrace various digital tools.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| 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 it