Institutional Factors Affecting Farmers’ Decision to Adapt to Climate Change
Bibliographic record
Abstract
Adaptation to climate change has become the global focal point especially in recent years. Researchers have defined adaptation to climate change as an effective way for farmers to survive in the face of the scourge. This paper investigated how institutional factors plays a role in farmers’ decision to adapt to climate change. The population of the study was emerging farmers drawn from five districts of the Limpopo province of South Africa. The study used a two-stage cluster sampling technique to select a sample size of 206 emerging farmers. The dependent variable of the study was farmers’ decision to adapt to climate change while the explanatory variables consisted of institutional factors such as access to extension services, farmers’ organisation, membership and access to climate change information. Binary Logistic Regression model was used to determine emerging farmers’ decision to adapt to climate change in Limpopo province. The results showed that institutional factors such as access to both weather information and extension services together with special training on climate change adaptation significantly influenced farmers’ decision to adapt. Therefore, the study recommended participation of different stakeholders to provide institutional support to farmers and thus enhance their extent to adaptation.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".