Analysis of The Effect of Pandemic Covid-19 on Economic Growth Using Mc Nemar Statistical Test
Bibliographic record
Abstract
The COVID-19 pandemic has affected the social, political sector and almost paralyzed the economic sector. In the second quarter of 2020, modern countries such as America, Singapore, Germany, France, and Italy have entered a recession. Many countries' economic growth has contracted due to the influence of restrictions on human movement. This study will examine the impact of the COVID-19 pandemic on Indonesia's national economy. Using Mc Nemar's test statistics shows whether the COVID-19 pandemic is affecting the Indonesian economy, especially the economy of 34 provinces. Statistical tests will also be used to see the effect of the COVID-19 pandemic on 17 categories in the GRDP of the Business Field. The study concludes from the results of Mc Nemar's statistical test that the COVID-19 pandemic affects the Indonesian economy and the economy of 34 provinces with a significance result of less than 0.05. Mc Nemar's statistical test also proved that 17 categories/sectors were affected due to the covid pandemic (significance below 0.05). Meanwhile, the sectors most severely affected are transportation, provision of accommodation and food and drink, company services, and other services. These four sectors had economic growth rates contracted to double digits when entering the second quarter of 2020.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".