The drivers and intensity of adoption of beekeeping in northwest Ethiopia
Bibliographic record
Abstract
Abstract Background Beekeeping activity is carried out in most parts of Ethiopia. However, despite the favorable agro-ecology for beekeeping practices and the high number of bee colonies the country is endowed with, the level of beekeeping adoption is low. Methods This study was conducted to identify determinants of the decision to adopt beekeeping, and the intensity of adoption by using a cross-sectional data collected from 772 rural households in Northwest Ethiopia. Stratified random sampling method was used to select the households, and the data were collected using a questionnaire. To achieve the objectives, Heckman two-stage sample selection model was used. Results The result of the first step Heckman model revealed that age and educational level of the household head, household size, extension visits, training, incentive, home consumption of honey, major economic activities of the household, perception towards better hives, distance to the nearest marketplace, the number of years the household stayed in the village, and location were the significant variables influencing rural households’ beekeeping adoption decision. The second step Heckman model revealed that livestock holding of a household head, number of extension visits, credit use, presence of honey bee pests, whether a household is engaged in swarm catching practices, and major economic activities of a household head were the variables that influence the intensity of beekeeping adoption significantly. Conclusions The findings of the study can be used to make evidence-based policy interventions to improve beekeeping adoption and the intensity of beekeeping adoption by rural households, which could also help to improve their livelihoods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".