The Defining Characteristics of Agroecosystem Living Labs
Why this work is in the frame
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Bibliographic record
Abstract
In response to environmental, economic, and social challenges, the living labs approach to innovation is receiving increasing attention within the agricultural sector. In this paper, we propose a set of defining characteristics for an emerging type of living lab intended to increase the sustainability and resilience of agriculture and agri-food systems: the “agroecosystem living lab”. Drawing on first-hand knowledge of case studies of large initiatives from Canada and France and supported by eight other cases from the literature, we highlight the unique nature of agroecosystem living labs and their distinct challenges with respect to their aims, activities, participants, and context. In particular, these living labs are characterized by exceptionally high levels of scientific research; long innovation cycles with high uncertainty due to external factors; and the high number and diversity of stakeholders involved. Both procedurally and conceptually, we link to earlier efforts undertaken by researchers seeking to identify urban living labs and rural living labs as distinct, new types of living labs. By highlighting what makes agroecosystem living labs unique and their commonalities with other types of living labs, we hope to encourage their further study and help practitioners better understand their implementation and operational challenges and opportunities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it