Bibliographic record
Abstract
The COVID-19 pandemic is sweeping the world, exerting a huge impact on the economic development and social order of all countries in the world. In order to understand the impact of COVID-19 on China's economy, the author will take the data of China's catering industry in eight specific months from the first quarter of 2022 to the second quarter of 2022 as the main sample to analyze the specific impact of COVID-19 on a single industry and pave the way for other studies to summarize the impact of the macro economy under the epidemic. In many cases, the economic problems of the catering industry also reflect the current situation of the individual economy in the whole market economy, which is particularly worthy of our research and thinking. In particular, the economic development of countries in the post-epidemic era is also an issue that cannot be ignored. Especially in developing countries like China, it is particularly important to maintain the balance between epidemic control and economic development. It is for this reason that the author thinks it is necessary to carry out some exploration and research in this aspect, and so as to lay a foundation and provide direction for the deeper research, so as to provide the adaptability and preparation that the catering industry needs to make in the face of the normalized epidemic, so that the market can find a way to bring benefits to society and the industry in the difficult situation. In the research and analysis of this paper, it is found that the impact of the novel coronavirus pandemic has had a lot of negative impacts on the catering industry. This paper will also conduct comparative analysis according to the specific time nodes of the epidemic in China and the specific data of the catering industry at specific time points in order to make the data more direct and comparable to draw a correct and convincing conclusion. This paper will deeply explore the impact of the novel coronavirus epidemic on China's catering industry, and provide some references for the development direction of the food industry under the epidemic. Finally, the conclusion of this paper is that the epidemic has had a large number of negative economic impacts on the food industry and, to some extent, put the catering industry in danger.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".