On the Enterprise Dynamic Management in the COVID-19 Pandemic
Bibliographic record
Abstract
With the worldwide spread of the novel coronavirus (COVID-19), the global economy has entered a cold winter, and the International Monetary Fund predicts that the global economy will shrink by about 3% in 2020. The outbreak of the epidemic has also caused heavy losses to the Chinese economy. In the first quarter of 2020, actual GDP fell sharply for the first time by 6.8% year-on-year for the first time. This is the first decline since record. Then, according to the data from the business survey in March, China's economy has improved compared with February, which shows that the economy has rebounded under the influence of policies. Judging from the current situation, although China has passed the peak period of the epidemic, affected by the high cases abroad, it can only be carried out slowly for the resumption of production. Enterprises, as microindividuals under the macro economy, need to pass through analyzing the dynamic management of the enterprise to deepen the reform of the commercial system and stimulate the vitality of the enterprise. This will also provide data support for the government formulating relevant policies, which is conducive to the synergy of various policies and enhance the momentum of economic recovery. On the other hand, we choose tourism as our specific research object. Thus, we need to set different scenarios according to the development situation of the epidemic, evaluate the impact of the novel coronavirus epidemic on China's tourism industry, and discuss tourism development and opportunities in the post-epidemic era from the aspects of tourism's response to the epidemic and the development trend of the tourism after the epidemic4.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".