Centering farmers’ perspectives in assessing the resilience of food farming in rapidly urbanizing regions
Bibliographic record
Abstract
Alarmed by farmland conversion, growing food insecurity, and increasingly threatened resources, multi-stakeholder groups endeavor to improve access to fresh food and protect farmland’s multiple community benefits. To inform the allocation of scarce resources needed to sustain local food production, this transdisciplinary action research investigated farm-level resilience within a fragmented county context. What will be needed to retain and enhance local food production capacity for the long term? Iterative analytical approaches utilized multiple data sources framed by agroecological resilience principles. Immersion in the local food movement, as a researcher, consumer, educator, and farmer advocate, offered ample participant observation opportunities across the Portland-Vancouver Metropolitan Region. Primary data also included semi-structured interviews and farming system assessments on 23 farms and two farmer-only roundtables. Analysis of public data compiled from multiple sources documented the high rate of farm turnover, a steady loss of agricultural capacity across all operational scales, and data insufficiencies. While direct-to-consumer (DTC) markets and supportive institutions strive to improve farm viability in urban regions, even DTC farms are only marginally resilient, at best. My dissertation research found an urgent need to redesign local policies, public institutions, and support networks in accordance with stated farmer needs. A pandemic-response assessment informs the next phase of collaborative action research by centering grassroots-led solutions forwarded by Black, Indigenous, People of Color (BIPOC) communities. How does equitable food-oriented development, aligned with BIPOC food sovereignty goals, serve to advance agroecological resilience and food system justice in metropolitan regions?
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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 | 0.000 | 0.000 |
| 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 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".