Comparison of restaurant inspection report results and its corresponding star ratings on Yelp and Google Reviews
Bibliographic record
Abstract
Background: In the current culture of dining-out, there is a greater emphasis on the overall dining experience at restaurants and less of a concern regarding food safety. The public often relies on consumer-generated review websites, such as Yelp and Google Reviews, to decide on where to eat. Each restaurant is often rated out of 5-stars based on factors such as customer service and food quality. The public perceives a restaurant with a 1-star rating poorly, whereas a restaurant with a 5-star rating is seen as excellent. Moreover, the aspect of food safety is determined by Environmental Health Officers (EHOs) who conduct inspections and assign hazard ratings to restaurants, which describe them as a low, moderate, or high-risk food premises. These inspection report results can be disseminated to the public online or through a placard system by the health authority. Currently, in most cities, there is no linkage or display of inspection report results on consumer-generated review websites. Methods: Secondary data was collected from publicly available online sources: Fraser Health’s restaurant inspection reports and two consumer-generated restaurant review websites – Yelp and Google Reviews. The author analyzed 170 randomly selected restaurants from the three most populous cities under Fraser Health’s jurisdiction (British Columbia, Canada): Surrey, Burnaby, and Abbotsford. Only independent restaurants and their routine inspection reports were considered in this study. The following data was obtained from each of the restaurant’s available routine inspection reports: current hazard rating, the average hazard score, and total number of critical violations (CVs). These variables were then compared to the current star rating found on Yelp and Google Reviews. Results: A total of six statistical analyses were conducted: two chi-square tests and four correlational analyses. When comparing the current hazard rating of the restaurant and their current star rating using chi-square tests, p = 0.0855 for Yelp and p = 0.0739 for Google Reviews. Furthermore, in all four correlational analyses, a negative linear relationship was observed, but only three resulted in statistically significant results. When comparing the average hazard score of the restaurant’s routine inspections and their current star rating, p = 0.0591 for Yelp (power = 47.21%) and p = 0.0000 for Google Reviews (power = 99.97%). When comparing the restaurant’s total CVs from routine inspections and their current star rating, p = 0.0001 for Yelp (power = 97.29%) and p = 0.0000 for Google Reviews (power = 100%). Conclusions: The findings of this study demonstrated that prescribed food safety evaluations largely align with the customer perception of restaurants. Although three out of six statistical tests resulted in statistically significant results, overall, it appears that restaurants with a higher star rating have lower number of CVs and lower average hazard scores. Even though this ideal relationship was established, the importance of safe food handling practices and serving safe food to the public should not be overlooked. Consumer-generated restaurant review websites are an excellent avenue to promote food safety within the overall culture of dining-out at restaurants.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".