Restaurant survival during the COVID-19 pandemic: Examining operational, demographic and land use predictors in London, Canada
Bibliographic record
Abstract
The COVID-19 pandemic placed considerable stress on restaurants from restrictions placed on their operations, shifting consumer confidence, rapid expansion of remote work arrangements and aggressive uptake of third-party delivery services. Industry reports suggest that restaurants are experiencing a much higher rate of failure in comparison to other sectors of the economy. Restaurant survival was assessed in the Middlesex–London region of Ontario, Canada as of December 2020 using a novel dataset constructed from public health inspection permits, business listings and social media. Binomial logistic regression models were used to determine the association of operational, demographic and land use factors with restaurant survival during the pandemic. Operations-related factors were considerably more predictive of restaurant survival, though some demographic and land use factors suggest that urban processes continued to play a role in restaurant survival. Restaurants that offered in-house delivery and phone-based ordering methods were considerably less likely to close. Restaurants with a table-based service model, drive-through or an alcohol licence were also less likely to close. Restaurants proximal to a concentration of entertainment land uses were more likely to be closed in December 2020. Closed restaurants were not spatially clustered as compared to open restaurants. The pandemic appears to have disrupted established theoretical relationships between people, place, and restaurant success.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".