Bibliographic record
Abstract
Abstract This study examines the effect of non‐farm labour participation on poverty reduction in rural Mauritania. Farm households with more land and livestock participate to a greater extent in non‐farm activities compared with households with smaller land or cattle. We study poverty's relationship with non‐farm labour activities in terms of the incidence as well as the intensity and severity of poverty. The study is the first to highlight the contribution of the non‐agricultural sector in the reduction of poverty in the rural areas of Mauritania. We apply probit, propensity score matching and inverse probability weighting techniques to determine the signs and impacts of participation on poverty reduction. The results show that the probability of being poor is 5.9% lower among households that have at least one member participating in non‐farm activities compared with those only associated with the agriculture sector. Participation in non‐farm activities is associated with lower intensity and severity of poverty (3.6% and 1.9%, respectively). We find that surplus labour released by the agriculture sector is absorbed in the non‐farm economy. Income generation through diversification into non‐farm activities therefore seems to be an effective way to reduce poverty in rural areas.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".