Factors influencing smallholder crop commercialisation: Evidence from Cte dIvoire
Bibliographic record
Abstract
In this study, we analysed factors that influence crop output commercialisation among smallholder farmers in Cote d’Ivoire. Unlike previous studies, we correct for sample selection bias by using the Heckman maximum likelihood sample selection model with village fixed effects. We rely on a unique and detailed dataset that covers 3,393 smallholder farmers. The dataset was gathered from the 2014 National employment survey collected by the National Institute of Statistics (INS) Côte d’Ivoire. Results from the study suggest that cooperative membership and land tenure security raise the level of marketed outputs of Ivorian farmers. Female headed households sold lower proportions of their outputs than their male counterparts. Labour shortage is a major constraint to crop output commercialisation. From a policy perspective, the Government should revive its interest in collective actions such as cooperatives, facilitate farmer’s access to credit, improve food crop productivity and enhance mechanization. Key words: Crop commercialisation, Côte d’Ivoire, smallholder farmers, agriculture.
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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 | 0.004 |
| 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) | 0.005 | 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".