The Impact of the Epidemic on E-commerce Industry
Bibliographic record
Abstract
Under the condition that the COVID-19 spread all over the world, the economy is affected negatively.The government published some policies to pretend people from the virus.At the same time, global commerce is affected.In this condition, e-commerce met its opportunity and risk.This paper contains some statistics and analyses to explain the development of e-commerce.During the related research, it is said that due to the spread of the virus, people tend to decrease their social activities and change their approach to shopping.When the demand for online shopping increases, people still worry about logistics delays, supply shortages, and food safety.The analysis of the environment includes policy, economy, society, and technology.Confronting with the virus, many countries published policies to face this epidemic and many companies tend to develop online shopping.Besides, people were more likely to purchase online with the development and spread of the mobile phone as well as the mobile payment.It is discovered that some factors, like advanced technology, free delivery, lower price, and reviews from other customers, can be used to explain people's activities.Eventually, some solutions are mentioned to solve problems.To solve the lack of commodities, the store owners tend to sell them in advance through live telecasts.Moreover, to reduce people's anxiety about the safety of frozen food, a mobile tracking system is supposed to be built up and companies' epidemic prevention situation can also be shown.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".