Portraying the Urban Food Environment of the City of Toronto before and during the COVID-19 Pandemic through Yelp Reviews
Bibliographic record
Abstract
The obesity epidemic encompassing diet-related health outcomes, such as obesity and overweight, has risen as a public health crisis. The urban food environment, within which we make our daily food choices, is essential in influencing eating behaviours. Recent impacts of the COVID-19 pandemic on urban dwellers have signaled changes in the urban environment that ultimately affected food consumption habits. This study utilizes Yelp reviews to capture changes in the environment, and improve our understanding of the consumers’ perceived urban food environment in the City of Toronto. The list of food words that can be used to identify content in social media data is expanded, then a comparison of the environment before and during the pandemic in relation to neighbourhood characteristics is performed. Results reveal that the food environment is disproportionately affected, and consumers’ favourableness towards healthy foods varied with neighbourhood characteristics. These findings should be considered for policy interventions catering to consumers’ characteristics.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".