Evils of knowledge sharing and learning: The case of agri-food misinformation in virtual communities of practices in Sri Lanka
Bibliographic record
Abstract
The emergence of virtual communities through social and online media has raised concerns regarding the dissemination of misinformation and its local and global impact on socioeconomic and political changes. Although numerous studies have been conducted on this topic in other domains, the extent to which misinformation affects the agri-food industry remains largely unexplored. This research aimed to fill this gap by investigating the prevalence and impact of misinformation in two popular Sri Lankan virtual communities of practice (VCoPs): Krushi Arunodaya and Turmeric, Ginger, Pepper & Cinnamon Cultivators’ and Buyers’ Association. Through qualitative research consisting of 16 key information interviews with group administrators and members, the study discovered that agricultural misinformation is rampant in Sri Lankan agri-food VCoPs, polarizing members on crucial topics such as organic farming, GMOs, and chemical fertilizers. The perception of misinformation and its dissemination is influenced by cultural, political, and societal factors, as well as individual personality traits and the need for self-expression. However, those with media literacy, knowledge, and experience are better suited to identify and avoid misinformation. The research also found that traditional media is involved in promoting agenda-based campaigns alongside social media and internet-based platforms. VCoP members recommended reporting and blocking as primary countermeasures to combat misinformation. Multi-stakeholder interventions by government, media, agricultural organizations, and VCoP moderators are necessary to prevent agri-food misinformation in Sri Lanka. Additionally, media agencies and experts should act responsibly in disseminating accurate information. • The problem of misinformation is gaining traction in the agri-food industry, but it remains largely underexplored. • Sri Lankan Facebook groups spread misinformation regarding organic farming, GMOs, and fertilizers. • Traditional media also contributes to the spread of misinformation by promoting biased campaigns. • Socio-cultural factors & farmer individual traits drive misinformation. • Multi-stakeholder approach needed to combat agri-food misinformation.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.021 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.004 |
| 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".