A Survey of Urban Agriculture Organizations and Businesses in the US and Canada: Preliminary Results
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This report summarizes the results of an online survey, conducted during February and March 2013, of 251 groups involved with urban agriculture (UA) projects in approximately 84 cities in the US and Canada. This is only a preliminary report. As such, we present descriptive statistics rather than a interpretive analysis of the survey responses. Furthermore, it is important to recognize that these results are not necessarily representative of all urban agriculture businesses and organizations across North America. Nevertheless, these results point to certain trends and patterns that offer rich opportunities for further inquiry. Our preliminary results reveal that the UA landscape is highly diverse. From beekeeping on balconies to vegetable production on multi-acre farms, UA incorporates a broad range of practices on a diversity of types of urban spaces across North America. Survey results also reveal the wide diversity of groups practicing UA, from businesses to non-profits to public institutions to informal collectives. These groups vary in size; some are entirely focused on UA work, while for others, UA is a secondary activity. We highlight some of the differences in how these groups practice UA, and how these practices vary between cities. Groups face many similar challenges in terms of funding, labor, and access to space, but certain barriers and needs are greater in some cities than in others. Funding for UA projects – if there is any at all – can come from many different sources and, in some cases, the source of funding impacts the type of UA practiced. Finally, the motivations of groups practicing UA are diverse. While groups frame their engagement in UA a variety of ways, however, interest in community building, education, food quality, and sustainability drives most UA practice among our respondents.
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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 | 0.000 | 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 it