Socio-demographic determinants of farmers’ beliefs about climate change cause in the Sudanian zone of Benin
Bibliographic record
Abstract
Understanding farmers’ beliefs on climate change is crucial as it drives the adaptation strategies that they might adopt. This paper investigates farmers’ beliefs on climate change in the Sudan Savannah Zone of Benin, a region heavily reliant on rain-fed agriculture. The multinomial logit model is applied to cross-sectional data collected through a survey of 60 randomly selected farm households. The findings suggested that 33.33%, 31.67%, 21.67%, and 13.33% of the farm households believe that climate change is due to human activities, to natural changes in the environment, gods anger, and to both human activities and natural changes in the environment, respectively. Moreover, the estimation results of the determinants of climate change cause indicate that the gender of the household head, the ethnic group, and household size influence significantly climate change beliefs. Based on the findings, information on the fact that climate change is not only due to natural changes in the environment, but is also due to anthropogenic greenhouse gases should be provided to farmers. This paper contributes to the literature by analyzing what farmers believe as causes of climate change which is beyond climate change perception. Moreover, the variable ethnic group and household size are found for the first time to our knowledge to determine climate change beliefs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".