Understanding Gendered Dimensions to Agricultural Misinformation on Climate Change Adaptation in Nigeria
Bibliographic record
Abstract
Reliable information is essential for climate change adaptation and food security. However, the spread of misleading agricultural information, including through digital platforms, can negatively influence farmers' decisions to adopt climate change adaptation practices and hinder agricultural productivity. This study examines Nigerian farmers’ responses to inaccurate or misleading information concerning climate change adaptation in agriculture. Using the Social Cognitive Theory (SCT), the study draws on data from an online survey and semi-structured interviews, analyzed through descriptive statistics and thematic coding using NVivo. The findings reveal that female farmers were more likely to attribute the spread of agricultural misinformation to uncertainty rather than to malicious intent. Observational learning plays a central role, as farmers model behaviors based on trust and engagement in social networks. While both men and women share misinformation, female farmers are less likely to spread agricultural misinformation once they identify it, though they are more inclined to remain passive upon receiving it. These gendered dynamics emphasize the importance of designing agricultural communication strategies on climate change that reflect how male and female farmers differently engage with information. Promoting critical thinking and fact-checking behaviors among all farmers, regardless of gender, is also essential to mitigating the impact of agricultural misinformation on climate change adaptation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".