Exploring co-production through engagement between scientists and producers in an agricultural living lab: A case study in Canada
Bibliographic record
Abstract
Agricultural living labs are initiatives where agricultural researchers work with commercial producers to test innovations and management practices under real world scenarios. In Canada, living labs across aim to use a co-production model across the research design and implementation cycle. This model is meant to combine the knowledge and experiences of producers, researchers, and key industry stakeholders. While a key component of co-production is engagement between producers and scientists, this process has not been widely studied in living labs. We developed a concept map for researcher-producer engagement based on identified success factors for living labs and used this to interview participants in Living Lab New Brunswick (11 agricultural producers and 3 scientists). Our results highlight the trade-offs of high trust in producer engagement in living labs and the influence of programmatic design features in informing engagement. Ultimately, our results showcase the challenges of building early engagement in co-production processes and how structural processes such as project scale and institutional incentives can complicate collaborative research. Living labs represent a collaborative research approach that aims to co-develop, test, and evaluate relevant practices to producers. Our results showcase the design and institutional opportunities and challenges in building engagement for co-production, providing considerations for other practitioners building engagement in co-production processes with rural agricultural communities.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.035 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".