Reallocation of Space for Outdoor Dining: An Analysis of COVID-19 Pandemic Outdoor Dining Policies and Perceptions in Ontario
Bibliographic record
Abstract
In recent years, the COVID-19 pandemic created disruptions in the restaurant industry. Consequently, cities in Ontario developed pandemic-induced patio policy with the goal of allowing restaurants to continue operation under lockdown restrictions. Pandemic-induced patio policy was identified to have the potential to contribute to long-term changes in these areas. Despite the increase in policy development, there is a gap in literature when considering how Ontario cities developed their policies and what the dominant themes of these policies are. Through a qualitative, mixed methods approach this thesis explores the changes that were made to patio policy in Ontario during the COVID-19 lockdowns of 2020 and 2021. The first manuscript assesses the extent that pandemic-induced patio policy was developed in the 52 cities in Ontario and what the key features of these policies were. The second manuscript explores how changes to patio policy were perceived by participants in the food retail environment. The first study concludes that supporting restaurant businesses through patio policy development was heavily prioritized by cities in Ontario during the COVID-19 pandemic. These policies varied in terms of time frame and method of implementation. Major policy themes included financial incentives, changes to the application process, and development of city-specific policy features including road closures, as well as other programming including promotional programs. A total of 10% of cities in Ontario implemented elements of their new patio policies post-lockdowns. Additionally, these policies were exempt from public consultation requirements, however some cities chose to conduct community engagement. The second study concluded that patio policy was a prevalent topic for employers, employees, and stakeholders when discussing responses to the COVID-19 pandemic. Themes discussed in interviews varied between the interview groups. Of the different respondent groups, employers discussed patios and patio policy most frequently. They found patios policy to be supportive, and that patio dining during the pandemic contributed to profitability. For some employees, patios lead to concerns over safety and create negative workplace environments. The feasibility of patio policy was perceived to be influenced by factors including vehicle use on the street, availability of patio space, and the business’s financial situation. Recommendations based on the findings are associated with developing patio policy in a holistic manner, which considers compatibility with current streetscape functions and relevant plans.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".