Learning and Innovation Competence in Agricultural and Rural Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it