Factors Affecting Pork Consumer Demand During The Covid- 19 Pandemic in Medan City, North Sumatra Province
Bibliographic record
Abstract
At the end of 2019, people in Medan City were shocked by the death of thousands of pigs in several districts in North Sumatera caused by African Swine Fever (ASF). In early March 2020, the government enacted the Large-Scale Social Restrictions (PSBB) policy due to the Covid-19 pandemic. These conditions caused an economic contraction marked by the growth of the national Gross Domestic Product (GDP) which fell sharply in the second quarter of 2020 against the second quarter of 2019 by 5.32% (y-on-y). This study aims to analyze the availability and price of pork and the factors that affect consumer demand for pork during the Covid-19 pandemic in Medan City. The research location consists of six traditional markets in Medan City that sell pork, namely Kanpung Lalang Market, Sunggal Market, Melati Market, Sambu Market, Sambas Market, and Sukaramai Market with purposive sampling method. There were 80 respondents. The data collection methods used were observation, interview, and literature study. The data processing and analysis method used is the Classical Assumption Test and Model Fit Test. The results showed that the total demand for pork before the Covid-19 pandemic was 98 kg while the demand for pork during the Covid-19 pandemic decreased to 40 kg. The decrease in demand for pork is due to the increase in pork prices caused by the outbreak of ASF disease in pigs in North Sumatra. However, the purchasing power of the people of Medan City decreased due to a decrease in income caused by Covid-19. Based on the results of the study, it can be concluded that during the Covid-19 pandemic there was a very drastic decrease in demand for pork with a percentage reaching more than 50%
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".