A Case Study Comparison of Direct Selling Alternative Food Networks in Belo Horizonte, Brazil and Toronto, Canada
Bibliographic record
Abstract
This thesis examines two local direct-selling AFNs: one in the Global North, in Toronto, Canada, and the other in the Global South, in Belo Horizonte, Brazil. It considers the experiences and roles of farmers within these two AFNs, using the findings to assess the potential and limitations of these two networks in what are two very different geographical locations. A comparative case study approach was taken, using a qualitative methodology. To the researcher’s knowledge, the comparison of a Northern AFN to a Southern AFN has not been documented, and thus this study provides a unique opportunity to observe how the AFNs are similar and how they are different in these two distinct locations. Data was collected primarily through qualitative interviews with farmers in each AFN case study, together with direct observations at points of sale. The findings from this thesis demonstrate that common assumed narratives about AFN farmers, that they are small scale, environmentally sustainable and socially just, are more complex than the literature suggests when held up to the varied reality of farmers’ experiences and livelihoods. The valuation or devaluation of local food shapes farmers’ successes at market, along with the economic, political, and physical spaces for farmers within each city. In Toronto, farmers both benefit from, and co-construct, narratives that value local food, as they cater to predominantly elite consumers. In Belo Horizonte, farmers attempt to divorce their food from its local origins by ‘sterilizing’ it to relate it to supermarket food. Each AFN model privileges certain groups to the disadvantage of others. The Toronto case study AFN focused on the participating farmers to the exclusion of low-income consumers, and the Belo Horizonte case study AFN focused on the low- income urban consumer to the detriment of participating farmers. This thesis makes a contribution to the AFN literature by providing a Southern perspective to a literature that has been predominantly Northern-based, including demonstrating that assumptions about valuation of the ‘local’ and of an elite class of consumer within AFNs do not hold true in the case of Belo Horizonte.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".