Bibliographic record
Abstract
Abstract Recent studies on asset poverty of the elderly in South Korea have widely used the cross‐sectional method. Yet, the cross‐sectional approach is limited of use for understanding an important feature of poverty, such as changes in the poverty condition of particular individuals, as they grow older. To understand the actual living condition of the elderly over time, using the Korean Longitudinal Study of Ageing (KLoSA), I examined the asset poverty condition of the elderly between 2006 and 2016 from both cross‐sectional and longitudinal approaches. In consideration of meaningful differences between different age groups, I divided the elderly population as "middle‐aged," "young‐old," and "old‐old." When using the cross‐sectional approach, findings showed that there were no substantial differences in the asset poverty across age groups although the income poverty rate of the old‐old was three times higher than that of the middle‐aged. Longitudinal analysis revealed that wealth mobility was more likely to occur among relatively younger age groups, and older people have difficulties in improving their poverty conditions over time. Our findings suggest that for the asset poor in the old‐old group, it is necessary to enhance the social security system in South Korea.
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 | 0.001 |
| 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) | 0.001 | 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 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".