Perception Driven From Farmers’ Socio-economic Characteristics Towards Tackling Climate Change
Bibliographic record
Abstract
Climate change is part of us and we are unlikely to avoid its impact. Impacts of climate change are severely felt within the agricultural sector, the division that strives to ensure food and nutrition security and contribute towards sustainable development in many of the developing countries. The only way farmers can survive in the face of climate change is through adaptation, a phenomenon that needs collaborative efforts. One of the critical driving factors of climate change adaptation is farmers’ perception towards tackling it, the focus of this paper. The main objective of this paper was to assess farmers’ perception towards tackling climate change. The population of the study was emerging farmers drawn from five districts of the Limpopo province, 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’ perception about tackling climate change while the explanatory variables consisted of their socio-economic characteristics such as age, gender, farming experience and level of education. Discriminant Analysis was used to draw comparison between groups of farmers who perceived that climate change can be tackled and those who believed it cannot be. The results showed that socio-economic characteristics such as formal education, agricultural education, age group, farming experience and off-farm occupation significantly contributed towards farmers’ perception regarding tackling of climate change. Therefore, the study recommended that stakeholders should facilitate linkage of farmers with varied sources of support.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".