Managing Knowledge During Partnerships: A Case of Intermediaries in Agricultural Innovation System.
Bibliographic record
Abstract
Within any agricultural innovation system, three key actors are well recognized. By default, the researcher is responsible for developing new knowledge, technologies, and innovations; the agricultural extension agent for delivery of the products; and the local farmer as the consumer. However, due to the challenges with most national agricultural extension systems in the developing nations, wide knowledge gaps have arisen between knowledge productions and use thereby limiting the successful functioning of this linear model of operation. This has led to the emergence of new and multiple intermediaries within most national agricultural innovation systems with the aim of bridging these knowledge gaps. The paper aims to show that the solution to this form of knowledge barrier is beyond the mere presence of multiple intermediaries, and involves coordination and collaboration roles among partners. It further identifies the potentials of the new information and communication technologies (ICTs) to enable the functions of these actors once the necessary social processes are put in place. The paper concludes by highlighting the critical role of systemic approach to innovation through a theoretical construct – ‘knowledge brokering role (KBR)’, that could help in role coordination among the stakeholders involved in the innovation process.
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.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".