First round Dataset: Impacts of Improved Bean Technology on Smallholder Productivity and Food Security in Burundi
Bibliographic record
Abstract
In July-August 2019, CIAT-PABRA and ISABU collected household-level data in Burundi to evaluate a bean research project funded by Swiss Development corporation (SDC) and Global Affairs Canada. This was the first impact assessment for the project and analyzed the adoption of improved bean varieties and their effects on yields and food security, using farmer recall. For variety identification, the study combined farmer recall with DNA fingerprinting. However, the initial impact analysis in 2020 was based on farmer recall, as the DNA results were not available at the time of publication. Methodology:The data were collected by researchers who had good knowledge in French and kirundi and possessed extensive experience in bean production context of Burundi. The team trained for 5 days in the questions written in English. Prior to the actual survey, the questionnaires were translated from English into French, uploaded onto tablets and pretested on few selected farmers. A community questionnaire was administered to key informants (i.e. village leaders and elders) during the first round of the survey and gathered information on the village level variables including the institutional aspects, such as access to information, seed, seed distribution programs, credit, roads, and market infrastructure and cultivar changes, and agro-climatic shocks. Data for the entire impact assessment were collected in two rounds: • Round 1 (July-August 2019): Focused on farm characteristics, adoption of improved bean varieties, production data, and a 7-day recall of household food consumption. The dataset includes information at household, plot and village level. Data set were collected around six modules as follows: 1) Household & Location: Demographics, assets, housing, and social networks, 2) Agricultural Practices: Bean varieties, cultivation methods, inputs, harvests, and market factors, 3) Institutional environment: Access to institutional services and credit, 4) Gender: men and women access to information and food intake by women in reproductive age groups and children below 5 years, 5) Post harvest: utilization and marketing. 6) Preferences & Food Security: Bean trait preferences and food security indicators. • Round 2 (Nov-Dec 2019): Assessed food and nutrition security through detailed dietary intake recalls for women of reproductive age (24-hour) and the entire household (7-day). This data set will be published separately
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".