The Review of Online Shopping Trend During and After the Covid-19 Pandemic
Bibliographic record
Abstract
pandemic has strictly affected the real economy, with the strategy of lockdown.Most people turn to online shopping during the lockdown, which largely developed the digital economy.This essay reviews papers about consumers' online shopping behaviors during and after the pandemic in different countries.Since most people shopped online during the serious duration of the Covid-19 pandemic, they are still using this shopping method even after the lockdown because they were getting used to the convenience of online shopping.People are shopping online for several reasons, the main reasons are fear of being infected, necessities, and the willingness to purchase.Moreover, there are a few factors that can affect people's decisions about online shopping, the main factors are age or generations, lockdown strategies, and the awareness of Covid-19 pandemic.Thus, we can predict that the future trend of shopping method would be online shopping, not only for goods, but also groceries and essentials.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.009 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".