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 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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".