COVID-19: DIFFICULTIES FACED BY CHINA’S RETAILING INDUSTRY AND RECOMMENDATIONS TO OVERCOME THEM
Bibliographic record
Abstract
Since the spread of COVID-19, the world’s economy has been seriously affected with the retailing industry being one of the hardest hit industries. China is one of the earliest countries to experience the virus outbreak and economy depression in the first quarter of this year and its retailing industry has gone through many challenges which witnessed a change in consumers’ buying decisions. For this reason, it’s very necessary for marketers to think about how to respond to the changes taking place in the retailing industry and overcome their shortcomings in the new era. In this paper, we will describe the difficulties faced by the retailing industry in China and discuss some recommendations that can be undertaken by the retailers, small and big alike, to better manage their business in the time of Covid-19. The recommended measures can serve as a guide for other countries to alleviate the impact caused by the coronavirus.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".