The impact of COVID-19 on Canadian restaurant operations and the likelihood of pivoting off-dining options post-COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic uncovered the weaknesses in the global food system, disrupting food production, processing, distribution, and consumer behaviors. The study seeks to examine the state of recovery in the restaurant operations, changes in supplier relationships, and the likelihood of pivoting off-premise dining options post-COVID-19. Based on primary data from 181 restaurants in Canada, the findings revealed that takeout was the most widely used off-premise dining option by the restaurants in the study before and during the COVID-19 pandemic. The most significant change during the pandemic occurred in the use of curbside pickup and delivery through third-party aggregators. The use of the drive-thru option remained at the pre-pandemic. The pandemic significantly impacted restaurants' sales and traffic levels and permanently closed some restaurant units. As of September 2021, two-thirds of the restaurants reported having recovered more than 50% of full capacity. The nature of supplier relationships during COVID-19 and pre-pandemic firm characteristics (number of restaurant units, business form, and restaurant category) influenced the likelihood of pivoting one or more off-premise dining options post-pandemic. By controlling the effect of pre-pandemic firm characteristics, the study provides empirical evidence on the state of recovery in restaurant operations and the likelihood of pivoting off-premise dining options post-COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".