Agricultural Communication for Addressing Climate Change Challenges: Understanding Farmers' Responses to Misinformation
Bibliographic record
Abstract
The successful adaptation of agricultural practices to address climate change challenges and ensure food security depends on access to accurate and reliable information. However, the quality of available information could hinder this process. This study investigates how Nigerian farmers navigate contradictory or confusing information related to climate change and food security. An online survey and semi-structured interviews were used to explore the actions farmers take when faced with such information and the underlying motivations driving their choices. Descriptive analysis, Multinomial Logistic Regression, and Binary Logit Model in SPSS were used to analyze the survey, while thematic coding in Nvivo was used for the interviews. The results reveal that farmers are more likely to encounter inaccurate information within their peer networks, highlighting the challenges associated with informal knowledge transmission. Farmers exhibit a range of responses upon receipt of such information. Notably, 40.16% of farmers disseminate the information further through face-to-face interactions, group discussions, or social media platforms. However, only half of these farmers verified the accuracy of the information before sharing it. Other observed responses include withholding the information or simply taking no action, suggesting varying levels of engagement and trust with the received knowledge. These responses deepen our understanding of knowledge exchange within agricultural communities, especially where farmers unintentionally spread misinformation. Global implications also exist for developing effective communication strategies to drive sustainable agri-food systems.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".