Geographies of capital formation and rescaling: A historical‐geographical approach to the food desert problem
Bibliographic record
Abstract
Although the “food desert” concept has captured the public imagination and spurred public policy efforts in many North American cities, the term has been critiqued by academics for being definitionally and methodologically vague, and for providing an incomplete picture of the complexity of food access. Rather than dismiss the study of urban, inner‐city food deserts, however, scholars can study disparities in retail food access through a historical, critical political economy lens to understand underserved retail landscapes as a product of capital formation and rescaling over time. The purpose of this article is to conduct such an analysis, using the case study of a low‐income community in Kingston, Ontario. Using historical research and qualitative interviews, the major finding of this analysis is that the physical accessibility of retail food appears to have declined over time in relation to the capitalization of the retail food sector. An imperfect relationship can be outlined over three phases of Canadian urban economic history to suggest that the food desert problem emerged largely in the transition from a decentralized, small‐scale, and neighbourhood‐embedded retail food industry to the scaled‐up, disembedded industry that now dominates the landscape. This industry‐level rescaling is contributing to a new urban politics of class and consumption through subtle, everyday activities such as food shopping.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".