Food deserts, food mirages, and gentrification in Toronto, Canada
Bibliographic record
Abstract
Few studies have explored links between retail food environments and gentrifying neighbourhoods across cities. As populations shift and commercial development continues, unpacking "food gentrification" processes in low-equity areas is important for ensuring healthy and equitable food access across cities. To further investigate this potential phenomenon, we used both primary (retailer consultations) and secondary (Canadian census, Toronto Public Health food inspection, land use zoning) data to explore cross-sectional links between food deserts and food mirages, four measures of gentrification in Toronto using the GENUINE gentrification tool, and Business Improvement Areas (BIAs). Food deserts represented about a tenth (9.3 %) of all dissemination areas in Toronto while food mirages represented only 4.5 %. Yet, nearly half (45 %) of all low-equity areas in Toronto were food deserts and food mirages (affecting approximately 516,000 residents). All gentrification measures employed, including BIAs, were also significantly associated with the existence of food deserts (between 2.5 and 14.5 times higher odds, respectively) while only two were associated with the existence of food mirages after adjusting for commercial zoning (between 1.6 and 2.7 times higher odds, respectively). Surprisingly, food deserts, not food mirages, were more strongly associated with gentrification. Longitudinal research is needed to better capture and monitor the evolution of food mirages.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".