Social Differences in the Vulnerability and Adaptation Patterns among Smallholder Farmers: Evidence from Lawra District in the Upper West Region of Ghana.
Bibliographic record
Abstract
There is growing attention on socially differentiated stakeholder groups in understanding vulnerability and adaptation to climate change.However, empirical research on smallholder farmers in Ghana has not paid adequate attention to social differentiation among smallholder farmers.This study sought to assess the perception of vulnerability and adaptation strategies of socially differentiated groups of smallholder farmers to climate change in Lawra district, north-western Ghana.Gender and age axis of social differentiation are the major focus of this work.The study employed a mix method study design involving 8 FGDs and 160 questionnaire surveys among smallholder farmers.Kendell's W rank correlation was used to rank constrains identified, descriptive statistics and chi-square was used to determine adaptation patterns among different social groups.Results suggest that, smallholder farmers are not homogenous.Rather, males and females and youth and older folks differ in their perception of vulnerability and subsequent adaptation strategies.The results highlight the need for adaptation interventions that pay attention to different stakeholder needs in reducing smallholder farmers' vulnerability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".