Bibliographic record
Abstract
The COVID-19 pandemic has caused serious problems in South Korea that led to an economic recession, stunted national growth, a huge gap between the real estate market and the asset market, and job instability in almost all sectors. Like most countries around the world, South Korea has aggressively implemented economic policies to overcome the debilitating effects of the pandemic, actively pursuing policy countermeasures that focused on what it called the Korean New Deal. To measure the effects of the Korean New Deal on the revitalization of the nation, this research paper used a dynamic regression model to analyze its impact on the economy. Our research used panel data on South Korea’s resulting economic growth rate and the supplementary budget the government provided to attain it. Our analysis showed that the supplementary budget created by the South Korean government did have an effect on the quarterly economic growth rate compared to that of the previous quarter. However, compared to the previous year’s economic growth rate, the government’s supplementary budget investment was unable to augment the yearly growth rate. It is our hope that these findings and the analysis of these outcomes will contribute to the formulation and implementation of a more efficient set of economic policies by the South Korean government for overcoming the adverse effects of the COVID-19 pandemic on the nation’s economic life and well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".