Comprehensive Perception Approach of Adoption: Experimenting Hybrid Chinese Maize Varieties in Benin
Bibliographic record
Abstract
The probability of adoption of four Chinese Hybrid Varieties of maize is considered as a favorable perception for these varieties by actors. In order to understand the way of adoption, a panel of actors comprising producers, processors, traders, extension officers, local elected representatives and, above all, end-users, was used as enumerator to evaluate the behavior of those varieties in comparison to the reference maize varieties known as “local” in experiment plots during the vegetative, harvesting and processing phases. For each actor surveyed and for each introduced variety, the comparative index of appreciation (IA) was determined by the difference in perception scores with respect to each of the descriptors evaluated. The adoption of maize varieties within the sites surveyed was affected by the respondent’ social profile (title), the number of varieties already adopted by the respondent, respondent’s experience, age, educational background, membership to an association/organization and the site (research station). The estimation of adoption relative to probabilities (odds ratio) of each variety of maize from the binary logistic regression models revealed only one variety having more than one in two chances for being adopted. Unlike the adoption rate of maize varieties calculated after expensive dissemination efforts, the analysis of probabilities and determinants of adoption somewhat reduces research, pre-extension and extension efforts. The proposed approach allows for a flexible integration of research experiments and field extension concerns of the process of adoption by creating panels of stakeholders around research experiments on research stations.
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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 | 0.004 | 0.002 |
| 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 | 0.001 |
| 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".