Bibliographic record
Abstract
The Covid-19 pandemic has become one of the biggest shocks to the Indonesian economy. As a result of the pandemic, there was deep pressure on aggregate demand and aggregate supply as well, so the economic output decreased and was not at optimal output levels. This paper will show the business cycle or economic fluctuations that occurred during the Covid-19 pandemic. The method used in this paper is the decomposition of Indonesia's quarterly real GDP for the 2000-2022 period into the growth trend and short-term fluctuations using the Hodrick–Prescott (HP) Filter. The results of this study indicate that in the first quarter of 2020, Indonesia experienced an economic contraction and was followed by a recession in the following quarter. After that, economic recovery occurred and reached long-term optimal output after 1.5 years. The characteristics of a recession and economic recovery followed a W-Shaped due to new pressures during the recovery process caused by the Delta variant, so a new recession occurred. By looking at the business cycle of the economy, the government can implement appropriate policies both fiscal and monetary at each phase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".