DETERMINANTS OF PARTICIPATION IN INNOVATION PLATFORMS AND ITS SUSTAINABILITY: A CASE STUDY OF SUB-SAHARAN AFRICA
Bibliographic record
Abstract
Background. Innovation platforms (IP) are a set-up where a group of stakeholders that are somewhat interdependent are identified and invited to get together and interact in a forum for social learning. However, Sub-Saharan African researchers have recently paid very little attention to its participation. Objective. To investigate the determinants of participation in IPs and its sustainability. The study specifically outlines the socioeconomic characteristics of the farmers and identifies variables influencing farmers' participation in IPs and the sustainability of such IPs. Methodology. The study used a multistage sampling technique to collect its data. The data were analyzed using the Double hurdle count model. Results. The results of the first hurdle indicate that the decision to participate in IPs is significantly influenced by factors such as gender, age, household size, years of farming experience, number of female working-class members, young dependents, aged dependents, access to agricultural extension, and asset ownership. While the findings of the second hurdle model reveal that gender, age, marital status, years of schooling, the number of female members of the working class, the number of young dependents, the number of aged dependents, access to extension services, and asset ownership play a significant role in determining the sustainability of participation in IPs. Implications. The paper adds evidence for a better understanding of the determinants of participation in IPs and its sustainability. Conclusions. Based on these findings, it is recommended that institutional structures and programs that enhance farmers' education, the frequency of extension contacts, and farm income be implemented to sustain participation in IPs.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".