The role of farmer networks in supporting adaptive capacity: Opening the door for innovation and transformation in the Northeastern United States
Bibliographic record
Abstract
This article explores the role of farmer networks in building the adaptive capacity of small and diversified farmers in the Northeastern United States. Previous research suggests that farmers’ networks are the backbone of practical agricultural knowledge systems in the United States, 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 9 farmer organizations from Pennsylvania to Eastern Canadian provinces, this article examines how the emergence of new ideas and agroecological innovations are influenced by geography, network affiliation, and perceived agency. First, we use regression analysis to identify factors that influence the use of no-till on diversified vegetable and berry farms, which is an emerging innovation in this community. Our analysis shows that geography may not be a significant driver of adoption among the population we sampled, which contrasts with previous research on explanatory factors, yet affiliation with certain farmer networks was significant in predicting the use or intended use of the practice. This quantitative analysis is complemented by qualitative data from a series of focus groups in which farmers identify the characteristics of certain networks which support them in addressing new challenges. Farmers identified that networks support them in learning about new ideas, accessing resources, and engaging in creative problem-solving, through facilitation of spaces for exchange with peers and experts and being responsive to their emerging needs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 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 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".