Alternative vs. conventional food networks: A geospatial analysis in relation to neighborhood sociodemographic characteristics in Montreal
Bibliographic record
Abstract
"There has been growing interest in ‘alternative’ food systems in North America over the past couple decades, coinciding in part with concerns over increased distancing between food producers and consumers as well as scepticism over the ‘conventional’ food system. This interest has led to the expansion of alternative food networks (AFNs), which aim to connect farmers and consumers while increasing the ties in the local community. While the expansion and popularity of these networks is clear, their role in the urban food environment remains understudied. In this study, I aim to address this gap by examining the distribution of different types of food sources in Montreal, including how equitable the availability of AFNs is across neighborhoods with different s sociodemographic characteristics. Specifically, I categorised an existing spatial database of food businesses and organizations based on a ‘food network’ typology, then compared it to a multivariate classification of neighborhoods (census tracts) based on key sociodemographic characteristics by using the ‘k-means’ method. I then overlay the food network categories with the census tract clusters in order to explore their distribution based on sociodemographic attributes (i.e., prevalence of low-income households, population density, prevalence of recent immigrants). While a vast literature has considered social and economic aspects of urban food environments at increasingly fine scales, to my knowledge, none have compared the distribution of different food networks at a city-wide scale. My findings show some distinct patterns in the types of food sources occurring in certain neighborhoods in Montreal, offering a basis for further research to investigate the role of different types of ‘alternative’ food provision and their impacts within the food environments at the city-wide scale. "@eng
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".