The effect of COVID-19 on customer traffic: A case study of Food and Beverage stores in Erie County, New York
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
The declaration of the COVID-19 pandemic and the resulting lockdowns brought focus on the importance of the retail sector for community well-being. The restrictive government policies that were put into place to curb the spread of COVID-19 added pressure on retailers to adapt to the subsequent changes in consumption. This research, using a case study of Erie County in the State of New York (NY), investigates these changes in visitation patterns for a commercial service sector that was deemed ‘essential’ - food and beverage. This study uses mobile location data to identify variations in shopping patterns for independent and chain stores. The study finds that by comparing the pre-pandemic to pandemic, there were changes to visitation patterns over time and between retail types. While the study highlights the potential to use mobile data to study shifts in consumption behaviours, the paper also reveals several challenges in using such data.
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.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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