Assessment of Institutional Linkages and Information Flow within the Agricultural Knowledge and Innovation: Case of Dakahlia Governorate, Egypt
Bibliographic record
Abstract
Institutional linkages and information flow between agricultural organizations play a critical role in addressing sustainability issues and promoting agrarian innovation. The aim of this study was to evaluate institutional relations and information between the various actors within the agricultural knowledge and information system (AKIS). The study focused on eight actors within the AKIS in Dakahlia governorate of Egypt, namely policy, extension, research, agricultural cooperatives, higher education, secondary education, credit, and the private sector. Thus, the survey sample included 11 representatives of each actor with 88 respondents. Data were collected by a standardized questionnaire distributed online. The graph theoretical technique was used for the quantitative assessment of information flow and institutional linkages established among actors. The findings indicated that agricultural extension ranked first about their real cause and effect on the rest of the system, having a value of 7.95. Two critical information pathways within the AKIS sustained innovation outcomes: (1) higher education–extension–agricultural cooperatives, (2) research–extension–agricultural cooperatives. The results also revealed that agricultural cooperatives ranked second after the extension component on the extent of supplying information to other members in the AKIS, with a value of 4.8. In contrast, the highest component received information from other components (7.6). By analyzing institutional linkages and information flow, this article gives insights to policymakers on the mechanisms that still need to be strengthened and the information gaps between actors to address the challenges of sustainable rural development.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".