Bibliographic record
Abstract
Decades of research and experience with anti-poverty programs around the world have revealed that there is more to poverty than simply maintaining a certain income level. However, until recently, poverty analysis in Korea has been mostly based on income. This study examines the multidimensional living conditions of the poor and its causes in Korea by testing the association between the material hardship and asset and income poverty. Material hardship is a direct poverty measure to identify the poor as those whose actual consumption fails to meet the basic needs. The main purpose of this study is to contribute to our understanding of the living conditions of the poor and the causes of material hardship including food, housing, utilities, and health hardship. Using the binary logistic regression analysis, this study found that households who were poor only in assets (and not income) were more likely than households who were income poor but not asset poor to experience all types of material hardship except for food. This finding suggests that the asset poor are more vulnerable to material hardship than is estimated by the income poverty measure. We describe how future research needs to expand hardship measures to encompass various living conditions in relation to the current Korean social context. This study implies that policy responses to poverty could be improved to the extent they consider the type and amount of a household’s available economic resources.
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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 | 0.001 |
| 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) | 0.003 | 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".