Dual Challenge of Climate Change and Misinformation: How Misinformation Shapes Vulnerability and Adaptation in Rural Communities in Pakistan
Bibliographic record
Abstract
In climate-vulnerable regions such as Pakistan, timely and accurate information is crucial for agricultural decision-making. However, misinformation has become a significant barrier to climate change adaptation, particularly in rural Punjab where farming communities depend heavily on institutional services. This study reinterprets the Model of Proactive Private Adaptation to Climate Change (MPPACC) by expanding the concept of social discourse to include misinformation as a central influencing factor. The research investigates how misinformation shapes climate change perception, perceived vulnerability, and adaptive capacity among rural farmers in Pakistan, with the aim of improving understanding and informing policy for more effective adaptation strategies. A mixed-methods design was employed, combining household surveys, focus group discussions, and key informant interviews in a highly climate-vulnerable region. Quantitative data were analyzed using descriptive statistics and regression analysis, while qualitative data were examined through thematic analysis. The study was guided by an enriched version of the MPPACC framework. Results show that access to credible agricultural information improves farmers’ perception of climate trends, whereas misinformation—particularly from informal sources—distorts risk perception and heightens vulnerability. Offline misinformation negatively influenced temperature perception, while digital misinformation had a stronger effect on off-farm adaptation capacity. Overall, misinformation intensified perceived vulnerability and reduced adaptive capacity. The study extends the MPPACC model by demonstrating that misinformation functions as both a structural and cognitive constraint within social discourse. This reconceptualization highlights the importance of information ecosystems, not solely physical or economic factors, in shaping adaptation behaviors. Practically, the findings emphasize that strengthening extension services, promoting digital literacy, and countering misinformation through localized, trusted networks can significantly enhance farmers’ adaptive decision-making. Policymakers and development practitioners should prioritize accurate and accessible communication strategies as a core component of climate resilience efforts in rural settings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| 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".