The Effects of Human Mobility Restriction During Covid-19 Pandemic to Indonesia's Economy
Bibliographic record
Abstract
In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have implemented lockdown restrictions to reduce the risk of infection. However, this will have an impact on the economy of a country, including Indonesia. This study will analyze the effect of mobility restrictions on the economic growth in Indonesia during the pandemic in 2020. The data used are real-time data on community mobility report provided by Google. Data processing begins with factor analysis, followed by multiple linear regression. This study aims to model the changes in community mobility as exogenous factors affecting economic growth. As a result, restrictions on community mobility, particularly related to job factors, significantly affect the economic development of an area, particularly in the provinces of Java Island. The resulting model would explain 96.97 per cent of the variations in regional economic growth in Indonesia in 2020. Besides, this study predicts that 25 provinces will experience a recession in the third quarter of 2020. This forecast is the result of economic growth estimated using the current condition. Learning the association between mobility and economy is essential to understand how much restrictions or relaxations needed that can be appropriate to our economy during 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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".