COVID-19 and the food industry in Hungary
Bibliographic record
Abstract
The COVID-19 pandemic has wreaked havoc on all sectors of the global and local economies. It has been widely reported that the food industry has been one of most affected sectors of the economy. However, empirical studies are yet to quantify the extent of the impact of the industry and also how the various sub-sectors of the industry have been affected. Focusing on Hungary as a case study, this paper seeks to empirically quantify the extent of the pandemic’s impact on the food industry by answering the following question: How has the COVID-19 pandemic impacted businesses in the food industry? To answer this question the study employs descriptive statistics, correlation analysis and paired samples t-test to analyse quarterly turnover data of 27 businesses sectors of the food industry for the period 2016 to 2020. The study finds no significant difference in the mean quarterly turnover of businesses during the first year of the pandemic and the previous year (t= -0.0344; df=107; p=0.731). However, a trend analysis revealed that over the past 5 years, it was only during the first year of the COVID-19 that businesses in the food industry recorded quarter-on-quarter reductions in turnover. The worse affected businesses in the food industry were enterprises involved in the retail sale of beverages in specialized stores, event catering, restaurants and mobile food service activities. Our findings suggest that while the pandemic adversely affected some enterprises in the food industry, others flourished during the first year of the pandemic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".