Successful Businesses during a Pandemic. How to Thrive
Bibliographic record
Abstract
The world suffered a huge loss since the first quarter of 2020 when the COVID-19 crisis started. Most of the businesses’ activity collapsed and the economy fell dramatically down. Other businesses have been struggling over the past months due to the coronavirus pandemic – temporarily closing in the face of lockdowns, or keeping their doors open while drastically scaling back operations. However, even in this unpleasant environment, in which reined the uncertainty and many entrepreneurs had to shut down their companies, multiple businesses managed not only to survive but to flourish during the last couple of years, despite the circumstances. This paper’s objective is to analyze this tendency of some of the world’s biggest multinational corporations headquartered in different continents, namely North America, Europe and Asia. There are several sectors well represented in the process, such as e-commerce, courier, stock exchange, gambling and subscription streaming services (e.g. Amazon, AliExpress, DHL, Netflix). Such multinational corporations succeeded to be adaptable and resilient in order to stay afloat in these ever-changing times and, in this way, to increase their revenues during the pandemic, but also to expand their reach or even grow their market, getting to new customers right in the middle of the chaos generated by the COVID-19 pandemic. The study is based on the financial results of the successful businesses – top companies in their field.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".