Bibliographic record
Abstract
Most poverty research operates from a similar perspective, looking at the conditions of people during poverty. However, the Panel Study of Income Dynamics revealed that only a small number of welfare recipients receive it for an extended period, and a great number of welfare recipients receive it for only a short time. This has now become a focal point of further poverty research. Based on panel data from Chia-Yi County during the 1990-1998 period, we analyzed the duration of poverty spells and the distributions of completed and uncompleted spells of poverty. We also report results of our analysis on factors affecting escape from poverty among these households. The results of our analysis indicate that the median duration of poverty was 4.69 years, when cohort-based estimates were used. When the data on the spells of poor households was used, the median spell of poverty was estimated to be 3.97 years long. More than 50 percent of poor households escaped poverty within 4 years and only one-quarter of poor households were in poverty for over eight years. The results of comparing the completed and the uncompleted spells show that among the former the duration tended to be shorter than the latter. Among the completed spells, more than 30 percent of them ended within 2 years. However, at any given point in time, the bulk of poor households were those experiencing a relatively long-term hou t of poverty. When the proportional hazard model was used, the age of the heads of households, the number of years of schooling, and health status were found to be important factors contributing to the possibility of leaving poverty. Among the household characteristics, we found that the single-parent households and the number of household members with the ability to work directly affect the probability of leaving poverty.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".