Transitions in Cooperative Labour and the Constraints to the Adoption and Scaling-Up of Labour Intensive Agricultural Technologies
Bibliographic record
Abstract
The research presented in this paper stems from a collaboration between researchers in the Benin Republic, Nigeria and Canada who are examining the opportunities to enhance the sustainable production of under-utilized indigenous vegetables through the micro-dosage of synthetic fertilizer. Because micro-dosing is a labour intensive technology, and is time sensitive in application, we sought to better understand how the availability of labour, as affected by changes in cooperative networks, might affect adoption and scaling up opportunities. The systems of cooperative labour described in this paper reflect the culture and traditions of the Betammaribe people, residing in the village of Koumagou B in northwest Benin. Our results indicate that cooperative labour systems among the Betammaribe are in transition and are being influenced by seasonal migration, the financial demands of formal education, the use of oxen by those with relative wealth, and off-farm employment. These pressures have led to the atomizing of Koumagou B households and a concomitant decline in the availability of cooperative labour. Interventions designed to improve the livelihoods of smallholder farmers must not inadvertently perpetuate social and economic inequalities or disadvantage those most vulnerable. It is this possibility that warrants careful consideration as we contemplate the benefits of adopting and scaling-up new agricultural technologies in the future.
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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 | 0.005 | 0.012 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".