A Global Pandemic's Effect on the Retail Industry
Bibliographic record
Abstract
It is commonly known that the effects of the COVID-19 pandemic have impacted many industries, including healthcare, transportation, and leisure to name a few. One particular industry that seems to have been greatly affected is retail. With many individuals deciding to stay safe at home rather than go out shopping, this poses a question regarding how retail companies are changing their selling and promotional strategies. It is apparent that online shopping has become more popular. However, what may be less apparent is how retail stores are adapting. With fewer customers entering the stores, it is important to look into what companies are doing in an attempt to encourage more foot traffic. This project takes an in-depth look into how COVID-19 has affected the retail industry in terms of visual displays, shopping patterns, and marketing tactics. The main focus will be on how companies have changed these components of their strategy during the 2020 holiday season as compared to the 2019 holiday season. The fourth quarter of the year is oftentimes the most important period for retailers, for it is when they have the largest opportunity to make sales. From analyzing how visual displays, shopping patterns, and marketing tactics have been altered as a result of the pandemic, this project will conclude with inferences of what the future of the retail industry may look like, along with whether or not some changes in retail strategies and consumption patterns may be permanent or long-lasting.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".