Covid-19, urban economic resilience and the pandemic pivot: Toronto’s restaurant scene
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Restaurants, fundamental to Toronto’s urban and cultural economy, experienced significant disruption because of extended closures during the Covid-19 pandemic. We examine data harvested from Yelp Business Search Endpoint on restaurant openings and closures in Toronto between May 2020 and May 2021. Our analysis shows that, despite expectations to the contrary, more restaurants opened than closed during this time. Geographically, similar numbers of restaurants both opened and closed in the city’s downtown core, demonstrating that early pandemic predictions suggesting the end of concentration are exaggerated. Overall, restaurants and restaurateurs exhibited resilience during the pandemic. We attribute this resilience, in part, to an ability to pivot to takeout-friendly foods, digital ordering and delivery and because of government funding supports.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it