Inequality of wealth for never married women in Canada, Germany, Sweden, and the United States
Bibliographic record
Abstract
The impact of aging has become a global concern due to the increasing number of older people in many industrialized countries. Today there are more older women than any other time in history. Living longer may become a burden rather than a blessing if lived out in poverty. This study investigated the relationship between individual characteristic of never married older women and wealth. The survey data was collected by the Luxembourg Income Study (LIS) for this quantitative investigation. Three age cohorts (young 50-59, middle, 60-69, and old 70+) were examined with marital status, country (Canada, Germany, Sweden, and United States), and level of education in a sample of 5885 women. The findings of the study indicated that education, age and marital status were significant predictors of wealth in the US, Canada, and Sweden, although marital status was reversed for Sweden. While education and age were significant predictors of wealth for women in Canada, marital status was not a significant predictor. The results comparing education and age of never married women to married women were significantly correlated to wealth. Comparability is a source of controversy in social sciences and creates limitations for doing comparison of concepts on income distribution statistics.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".