Exploring Collaborative Innovation Approaches: Early Deliberations from the Living Laboratories Initiative
Bibliographic record
Abstract
In 2018, Agriculture and Agri-Food Canada developed the Living Laboratories Initiative, a network of agroecosystem living labs, to encourage the adoption and scaling up and out of innovation in both technology and practice in climate change adaptation and mitigation in agriculture. This paper explores living labs as a new collaborative innovation approach that can build trust and develop long-term relationships between different actors in the agri-food system. It answers the question: what can collaborative innovation approaches, like agroecosystem living labs, reveal about the needs of actors within the collaborative governance process? Using a combination of semi-structured interviews and participant observation, this study gathered early-stage insights from various agroecosystem living lab partners in two Canadian agroecosystem living lab sites. It argues that starting conditions of partners were particularly influential in developing living labs. To mitigate current and potential obstacles, metagovernance can be a way to maintain commitment from partners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".