La place des jeunes africains sur les périmètres irrigués dans un contexte de libéralisation et de partenariat public privé : le cas des jeunes agriculteurs du delta du fleuve Sénégal
Bibliographic record
Abstract
This article analyzes different approaches to establishing rural youth in irrigation areas in the Senegal River delta in the context of new hydro-agricultural developments and massive land acquisitions by agrobusiness. Government-led irrigation projects introduced in the 1960s distributed free 0.2-hectare plots of land to young workers from the surrounding region. However, in the 1980s, Senegal took a liberal turn that led to a shift from public-sector to private-sector initiatives. As a result, young workers, who often lack the means necessary to acquire land, have found it much more challenging to pursue farming in irrigation areas. Since 2006, few young farmers have acquired any of the 5,000 hectares of new irrigated farmland developed through programs based on the public-private partnership model. At the same time, agro-industry growth has provided young people with many temporary and precarious employment opportunities. Based on surveys of farmers and agricultural workers in the Senegal River delta, we show how young people find it increasingly difficult to acquire land or secure adequate employment in the agricultural sector. In addition, we demonstrate that access to irrigated land, as opposed to salaried agricultural work, represents the most effective strategy for poverty reduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".