Racial equity and the USDA's Office of Urban Agriculture granting program and urban offices
Bibliographic record
Abstract
Urban agriculture (UA) has long been practiced in the U.S. by socially disadvantaged and low-income people for the purposes of subsistence, community and resilience. Government support for UA, however, has waxed and waned, including in city and federal policy. The 2018 farm bill established the Office of Urban Agriculture and Innovative Production (OUAIP) with the mission to encourage and promote “urban, indoor, and other emerging agricultural practices” (Agriculture Improvement Act of 2018, Title XII, Sec. 12302). The inclusion of UA in federal agriculture policy was a welcome change for many urban farmers and gardeners who had long sought recognition of urban production. Yet, historical discriminatory policies and practices on the part of the U.S. Department of Agriculture (USDA) have led some farmers and advocates to be wary of the department, and may suggest reticence to engage with USDA programs. This brief shares key findings and policy recommendations from a study that sought to understand the roll-out of the OUAIP and connected programs through a racial equity lens. We used a multimethod data collection approach that included national surveys of UA stakeholders; in-depth interviews with UA stakeholders in two case study cities, New York City and Atlanta; informational interviews with Urban Service Center (USC) Leadership in cities with urban county USDA Farm Service Agency (FSA) offices; GIS mapping of publicly accessible data; review of relevant policy documents; and participant observation, including meetings of the federal-level Urban Agriculture and Innovative Production Advisory Committee (UAIPAC). The study was supported through the Socially Disadvantaged Farmers and Ranchers Policy Research Center (The Policy Center) at Alcorn State University. In this policy brief, we introduce the urban agriculture provisions in the 2018 farm bill in the context of historical discrimination within the USDA. We then provide a short overview of our 2023–2024 study exploring the establishment and outreach of these provisions among Socially Disadvantaged Farmers and Ranchers (SDFRs) urban stakeholders followed by the key findings. We conclude with a set of policy recommendations, and reflection on how these recommendations may be relevant in 2025 and beyond.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".