Participatory and community-based approach in combating agri-food misinformation: A Scoping Review
Bibliographic record
Abstract
The spread of ill information with or without the intention of deceiving or causing harm has negatively impacted agricultural development both in social and digital spaces. This has led to a lack of trust in adopting new technologies and practices, which has hindered the process of facilitating agricultural development. Although the study of agri-food misinformation is still in its early stages, this paper draws on a scoping review of existing literature and lessons learned from other fields, such as political science and public health, which have extensive experience in combating misinformation in social settings. The article explores how Farmer Field Schools (FFS), a popular participatory and community-based approach, can incorporate media literacy education and how a local agricultural information hub, platform approach and a relatively new approach called technology stewardship in agricultural extension can help those working in the agri-food industry combat misinformation.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".