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Record W4379876595 · doi:10.54254/2754-1169/6/20220193

The Economic Impact of COVID-19 on China's Catering Industry

2023· article· en· W4379876595 on OpenAlexaboutno aff
Xujiayun Deng

Bibliographic record

VenueAdvances in Economics Management and Political Sciences · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsOrder (exchange)ChinaQuarter (Canadian coin)BusinessPandemicFace (sociological concept)Coronavirus disease 2019 (COVID-19)World economyAdaptabilityEconomic impact analysisEconomic growthEconomyEconomicsPolitical scienceGeographyManagement

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.339
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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