Bean technology adoption and its impact on smallholder farmers’ productivity, bean consumption; and food security data, Zimbabwe
Bibliographic record
Abstract
This data was derived from the 2018 nationwide endline survey of a project that evaluated the adoption and impact of improved bean varieties in Zimbabwe. The country, a PABRA member, was a flagship for a 2014 initiative aimed at enhancing food security and incomes through bean research. The endline survey was implemented as an activity of the flagship initiative funded by Swiss Agency for Development and Cooperation (SDC) and Global Affairs Canada (formerly Canadian International Development Agency [CIDA]) through the Pan-Africa Bean Research Alliance (PABRA)/CIAT and the Southern Africa Bean Research Network (SABRN). The dataset was analyzed to determine the degree of influence project interventions had on bean production, the utilization of promoted technologies, and overall household welfare. Furthermore, the collection aimed to derive lessons on the efficacy and underlying reasons for the outcomes of various interventions. The data originate from a panel of households established in 2016, enabling a longitudinal analysis that accounts for time-invariant unobservable household characteristics. The dataset includes household, plot, and village-level information organized in modules: 1) Household & Location: (identification, Demographics, assets, and social networks), 2) Agricultural Practices (Bean varieties, cultivation methods, inputs, and harvests), 3) Land holding and utilization, 4) Field level data on bean area, production in previous cropping seasons 5) bean variety identification sample collection, 6) Institutional Access (Credit and agricultural services), 7) Post-Harvest & Markets (Bean utilization and marketing); 8) Non-chemical bean management strategies and post-harvest handling (ICM/IPM); 9) farmer preferences; 10) Food Security (variety Trait preferences and food security indicators) and 11) Income from others/none agriculture to your household. The data are organized into 19 separate files, each of which contains a common unique household identifier (hhid) that can be used to merge them Additional information was also collected at community level through focus group discussions, used to assess the extent of spillover effects by profiling direct and indirect intervention communities. By collecting data from the same households and communities, we aimed to address potential unobservable effects assumed to be fixed over the three years. Methodology:Trained enumerators collected data using a pre-tested digital questionnaire (CAPI) from the heads or spouses of the same households originally surveyed in 2016. This fourth-year, follow-up survey captured both household and agricultural plot-level information. To assess spillover effects, we also conducted community-level key informant interviews, profiling both direct and indirect beneficiary communities. This panel design allows for controlling unobservable, time-invariant characteristics over the three-year period.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".