A Survey of Urban Agriculture Organizations and Businesses in the US and Canada: Preliminary Results
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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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".