Learning and Innovation Competence in Agricultural and Rural Development
Bibliographic record
Abstract
Purpose: The fields of competence development and capacity development remain isolated in the scholarship of learning and innovation despite the contemporary focus on innovation systems thinking in agricultural and rural development. This article aims to address whether and how crossing the conventional boundaries of these two fields provide new directions for developing learning and innovation competence in international development. Design/methodology/approach: Using mixed methods research, this article assesses work environments for experiential learning and innovation, and investigates effective ways of enhancing core competence in agricultural research, education, extension and entrepreneurship. Findings: Findings suggest that while the focus on input and output indicators are relevant for technological innovation competence development, outcome indicators, such as measures of changes in cognitive, affective and psychomotor domains of learning and innovation, would better serve the purpose of developing organisational and institutional learning and innovation competence. Practical implications: This research concludes that crossing the conventional boundaries of competence development and capacity development serves as a way to renew the role of education within the innovation systems thinking. However, such an attempt to enhance human capabilities and functionings through education should integrate theory-based, competence-based and experiential learning components as a coherent whole. Originality/value: This article demonstrates the value of crossing the conventional boundaries of the two seemingly unrelated fields—competence development through education and capacity development through extension—to provide new directions to operationalise innovation systems thinking in agricultural education and extension.
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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 | 0.007 | 0.012 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.000 | 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".