Impact of the pandemic on the world's best brands
Bibliographic record
Abstract
The pandemic has negatively impacted thousands of businesses, but many global brand companies have adapted to the situation and have made great strides by changing their strategies. Global brand companies were able to increase their market capitalization from 12% to 565% during the pandemic and isolation. The article analyzes the market capitalization of companies included in the "100 best in the world" rating, the size of large companies in the region and its changes, changes in the market capital of countries such as Japan, UK, Germany, Canada, USA, France, China. In the course of the analysis, the author reviewed the reports of the “500 best companies in the world”, “100 best companies in the world”, materials of the World Economic Forum “World Competitiveness Index”. When analyzing the market capitalization of the best companies in the world, general logical methods were used to collect information and effectively search, group, process and summarize the necessary material, compare materials of international organizations and ratings, as well as the work of research scientists. According to the comparative method, the analysis was carried out on the example of the best US companies: Apple Inc., Microsoft Corp, Amazon. som Inc., Chinese giants: Tencent, Alibaba GRP-ADR, Kweichow Mouta, the best in Japan: Toyota Motor, Sony Group Corp, German companies like Volkswagen AG and famous French companies like L'oreal and others
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".