The role of social capital in addressing seed access constraints and adoption intensity: Evidence from Arsi Highland, Oromia Region, Ethiopia
Bibliographic record
Abstract
In Ethiopia, limited empirical studies exist on how seed supply access constraints condition adoption intensity (demand). Hence, this research employs the augmented Double hurdle model to incorporate the effect of seed access (local supply) constraints in conditioning demand. Moreover, nine factors were constructed of twenty-eight indicators using Principal Components Analysis to resolve which cognitive and structural indicators drive social capital at the farm household level. The Double hurdle result reveals that social capital indeed determines wheat varieties access; besides, different forms of social capital have dissimilar effects on varieties demand. In addition to social capital variables (such as getting well with other farmers, generalized trust, and trust in agricultural institutions), information on seed access, training on varieties selection, and education have significant positive effects on relaxing seed access constraints and demand . Hence, the result suggests that agricultural policy and extension efforts should consider not only human, and physical capital, but also social capital in relaxing seed access constraints and demand. Furthermore, the government of Ethiopia should develop strong regulatory mechanisms to reduce corruption in the seed supply system.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".