Bibliographic record
Abstract
The Covid-19 pandemic in Indonesia since March 2020 has had an impact on negative economic growth for two quarters, namely minus 5.2 percent in the second quarter and minus 3.49 percent in the third quarter (yoy). Likewise, the economic growth of Kulon Progo Regency is expected to experience a drastic decline in 2021, after 2019/2019 had the highest growth reaching 18 percent.This article aims to analyze impact the Covid-19 pandemic has on the economy growth of Kulon Progo Regency per economic sector. This article also projections Kulon Progo's economic growth until 2024 using eleven projection methods. From the results of the projections carried out, the Gross Regional Domestic Product (GRDP) of Kulon Progo Regency in 2020 is predicted to slow down due to the Covid-19 pandemic case which affects almost all sectors. However, this decline only occurred in 2020, because in the following year several sectors are predicted to increase in line with the improving situation and operation of infrastructure projects. Several sectors that will increase, such as the transportation and warehousing sectors, due to normal activities at Yogyakarta International Airport (YIA). Based on the optimistic, moderate, and pessimistic scenario, Kulon Progo Regency's economic growth will continue to grow positively. There are two contributions of this research, firstly analyzing the impact of the pandemic and projecting the impact for the next 4 years and secondly, the projection is carried out using the best model of eleven methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".