Adaptive strategies and local innovations of smallholder farmers in\nselected agri-food systems of central Kenya
Bibliographic record
Abstract
Food production in Kenya is closely related to smallholder agricultural\nproduction. Paradoxically, many smallholders suffer extended periods of\nfood crises. This underscores the importance of understanding the\nmultiple pathways smallholders use to deal with food insecurity.\nParticipatory action research, using both qualitative and quantitative\nmethods was undertaken to identify adaptation strategies and\ninnovations used to address food insecurity vulnerabilities. A sample\nof 360 households was drawn randomly from 18 farmers’ groups\nliving under acute food and livelihood crisis (Mbeere South district);\nexperiencing borderline food insecurity (Kirinyaga West District) and\nthose with low resilience (Nyandarua North District) all in Kenya.\nResults showed that smallholders in these areas use and perpetuate\ndiverse adaptive strategies and innovations for coping with\nvulnerability, for risk avoidance and for livelihoods insurance\nenhancement. These strategies and innovations ought to be recognised by\nresearch, development and policy actors and should inform interventions\nintended to strengthen smallholder agri-food systems in Kenya.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".