Exploring the Role of Knowledge Mobilization in the Adoption of Integrated Pest Management for Grapevine Viruses
Bibliographic record
Abstract
Adopting sustainable agricultural practices requires effective knowledge mobilization of research to growers to inform their decision-making. Grape growers are increasingly facing widespread viral outbreaks, causing reduced quality and quantities of grapes. Growers affected by grapevine viruses are recommended to adopt integrated pest management practices, which are complex and require substantial knowledge. Understanding how to mobilize knowledge effectively is critical to the adoption of sustainable agricultural practices; however, getting research to growers is challenging. To better understand the role of knowledge mobilization in adopting integrated pest management for grapevine viruses, a case study of the grape and wine region of Niagara, Ontario, was conducted. Study One aimed to synthesize the knowledge produced on grapevine viruses in Canada and the United States by conducting a scoping review. Eligible articles were collated and thematically analyzed to comprehensively review the literature and identify key themes. Seven themes emerged: the field of grapevine virus research is growing, research foci are shifting and responsive, knowledge of grapevine leafroll-associated viruses and future research needs, knowledge of grapevine red blotch virus and future research needs, research needs of other major viruses, primary virus management foci, and primary vector management foci. Study Two aimed to identify factors facilitating and hindering the flow of knowledge using interviews and observations of primary actors and knowledge mobilization events in the Niagara region. Data was thematically analyzed, resulting in six key themes: connectivity, intermediaries, and tailoring knowledge mobilization to growers’ needs facilitate knowledge mobilization, and lack of localized and practical knowledge, limited support and resources, and misalignments among actors hinder knowledge mobilization. Study One offers a valuable educational resource to understand what is known about grapevine viruses and where future research efforts are required while making the research more accessible to researchers, intermediaries, and growers. Study Two informs future agricultural knowledge mobilization efforts and provides scholarly input on the factors influencing knowledge flow. Together, these studies enhance Nguyen et al.’s (2017) knowledge-action framework, elaborating on existing factors and identifying additional factors influencing knowledge mobilization.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".