Assessing the communication network of a budding industry: rooftop urban agriculture in Toronto
Bibliographic record
Abstract
This research provides an analysis of the Rooftop Urban Agriculture (RUA) community in Toronto by taking an inventory of participants and key players, and illustrating the communication network surrounding this emerging community. A social network assessment is used to reveal network connections, and explore the level of cohesion of the RUA community in Toronto. Consideration is given to the implications that this has on its growth, representation, and potential areas of engagement with other factors, including municipal decision makers. Semi structured interviews reveal the driving values expressed by RUA practitioners and others involved with the practice, and the characteristics of this group’s formation. Based on this information, RUA is identified in this work as an emerging Civic Food Network(CFN). Based on the findings of the network assessment, this research argues that the lack of an organized and cohesive RUA Network is hindering the progress of this emerging group, and the potential for valuable knowledge sharing that would enable its growth. Finally, recommendations are provided to address how those practicing rooftop agriculture can seek to maximize knowledge sharing within the RUA community and build connections to the City of Toronto to strengthen this emerging CFN.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".