Fostering Inclusive Innovation for Agriculture Knowledge Mobilization in Sri Lanka: A Community-University Partnership Development Project
Bibliographic record
Abstract
Mobilization of scientific and indigenous knowledge in support of sustainable agriculture has been identified as a vital activity that faces numerous challenges today, yet constraints and limitations on traditional agricultural extension methods as well as high costs of information provision have been cited as barriers to improving the livelihood of farmers in developing countries, particularly those residing in the lowest socioeconomic category known as ‘base of the pyramid’. Information and communication technologies (ICTs) have long been regarded as forces for positive change in agriculture and rural development despite a track record of modest success with many initiatives. This paper describes the approach and initial results of an ongoing initiative involving researchers from Sri Lanka and Canada to create a community-university partnership intended to establish local capacity for inclusive innovation using low cost ICTs that can support knowledge mobilization within agricultural communities of practice. Initial results of the project point toward ‘technology stewardship' as a promising approach for building capacity for local innovation through a community-university research partnership.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".