Bibliographic record
Abstract
Following the Asian Financial Crisis of 1997, Korea has suffered what many consider to be a severe poverty problem. Despite policy efforts to reduce poverty and economic recovery in the early 2000s, poverty affects many households and certain households are at risk of staying in poverty once they are in it. Using longitudinal panel data from 2005 to 2014, this study defines three indicators of poverty based on asset holdings, rather than income. It then examines the dynamics of asset poverty in Korea across the study period. The study’s primary goal is to reveal differences across the three indicators and identify which groups of poor people in Korea have been structurally trapped in poverty. We applied a dynamic panel model of discrete choice to the Korean Welfare Panel Study (KOWEPS) from the 1st to 10th waves and show that, despite the indicator, the asset poor who experienced asset poverty in the previous surveyed year or at wave 1 are likely to fall into structural and persistent poverty over time. In addition, the probability of incurring asset poverty decreased with home ownership, higher disposable income, and greater diversification of the household portfolio. Future research should study the duration of asset poverty to complete a comprehensive picture of the asset poverty condition.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".