The spaces for farmers in the city: A case study comparison of Direct Selling Alternative Food Networks in Toronto, Canada and Belo Horizonte, Brazil
Bibliographic record
Abstract
The current focus of Alternative Food Network (AFN) literature in the global North overlooks the reality of Southern AFNs and the potential contributions from studying Southern case studies. In this research, we used interviews and observation to determine how the differing valuations of ‘local’ food and farmers in two case study locations, one in the global North (Toronto, Canada) and one in the global South (Belo Horizonte, Brazil), affected the physical, economic, and political spaces in the city for farmers participating in the AFNs. The geographical concepts of scale, space and place are central to understanding Alternative Food Networks (AFNs). Drawing on work by Cook and Crang (1996) on ‘geographical knowledges’, we examined how farmers and consumers reinforced and constructed different narratives of ‘local’ food, which was valued by affluent consumers in Toronto but not by affluent consumers in Belo Horizonte. In Toronto, farmers operated in physical spaces that put them in contact with affluent consumers, and they were able to take advantage of both at market and off market economic spaces. In Belo Horizonte, farmers were relegated to marginal physical spaces, and had limited economic and political power. There were broader social justice implications related to whether the AFN operated mainly within affluent or marginal spaces. These case studies demonstrate that the scale, space and place are actively constructed, and certain constructions privilege some actors over others in the AFN and within the city.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".