Role of Farmer Networks in Supporting Adaptive Capacity of Farmers in the Northeastern US
Bibliographic record
Abstract
My research explores the role of farmers' networks in building the adaptive capacity of small and diversified farmers in the Northeastern US. Previous research suggests that farmers’ networks are the backbone of practical agricultural knowledge systems in the US, serving as a critical venue where growers exchange and negotiate new ideas. Drawing upon empirical evidence from a regional survey on climate resilience and a series of focus groups conducted in collaboration with nine farmer organizations from Pennsylvania to Eastern Canadian provinces, this paper examines how the emergence of new ideas and agroecological innovations are influenced by geography, network affiliation and perceived agency. The collaborative approach used in this research highlights the importance of strategic problem structuring as critical to successful problem-solving and communication about climate change. Multiple theories of change in agriculture communities are applied to the dataset to illuminate the factors that influence the emergence of innovative ideas for adaptive agroecosystem management in the region. This research offers a Farmer’s First perspective on how agricultural communities change in the face of climate change and what resources they need to successfully adapt.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".