Effect of Irrigation on Poverty among Small-Scale Farmers in Limpopo Province of South Africa
Bibliographic record
Abstract
Despite the strength and stability of South African economy, poverty and inequality remain a glaring and persistent issue in the country. About 40% of the population live in outright poverty or continuing vulnerability to being poor, with poverty being more persistent in rural areas. The Forster-Greer-Thorbecke index and a Logit econometric model were used to measure the dynamics of poverty among irrigation and non-irrigation individuals and households. The poverty incidence, depth and severity were found to be higher among non-irrigation household than among irrigation households. In term of poverty depth, it will cost R51.08 per capita to eliminate poverty among small-scale farm families that practice non-irrigated, while R48.00 per capita will be needed to eliminate poverty among small-scale irrigation families.There was significant correlation between income poverty and capability and deprivation poverty. This Implies that policies aimed at mitigating income poverty may also mitigate capability and deprivation poverty.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".