Planning for complete communities: An analysis of food access in downtown Winnipeg
Bibliographic record
Abstract
Many North American cities are struggling with a phenomenon called a “food desert”, known as a particular area of a city that fails to provide its residents with access to nutritious food. Due to recent closures of grocery stores, this title of a ‘Food Desert’ is often applied to parts of Downtown Winnipeg. In light of current efforts to further develop Winnipeg’s downtown core, planners and developers are taking a closer look at the issues behind food access for urban residents. Much of the current literature on food access in urban settings focuses primarily on the challenges for low-income households. However, poor food access is also an issue for non-low income residents, who may not be dependent on convenience stores, but must drive long distances in order to purchase groceries, causing unsustainable shopping habits as well as a loss in local consumer dollars. This research focuses on the issues of food access from the perspective of a diverse urban community with a range of incomes. The research aims to address the gaps in food desert literature by providing a better understanding of the challenges behind food access for different types of residents in urban areas, as well as how improved food access can in turn contribute to a complete community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".