Bibliographic record
Abstract
Female economic empowerment – rising earnings, increased opportunities, greater labour force participation – has given many women the means to save. The shifting of responsibility for retirement security from employers and governments onto individuals has given women a reason to save. But are women actually saving? In this paper, we explore the relationship between the gender dynamics within a family and the accumulation of wealth. We find that little evidence in support of the conventional wisdom that families with a female financial manager save more and repay their debts more often. We find some evidence that male financial management leads to greater savings, and other evidence suggesting that savings patterns have a complex relationship with intra-family gender dynamics. El empoderamiento económico de la mujer – el aumento de los ingresos, mayores oportunidades, mayor participación laboral – ha dado a muchas mujeres los medios para ahorrar. Al pasar la responsabilidad de los ingresos de la jubilación de los empleadores y el gobierno a los individuos ha dado a las mujeres un motivo para ahorrar. ¿Pero realmente ahorran las mujeres? En este artículo se analizan las relaciones entre las dinámicas de género en una familia, y la acumulación de riqueza. Se ha llegado a la conclusión de que hay poca evidencia que apoye la creencia convencional de que las familias en las que una mujer gestiona las financias ahorran más y devuelven sus créditos más frecuentemente. Se ha encontrado alguna evidencia de que la gestión financiera por varones acarrea mayores ahorros, y otras evidencias que sugieren que los patrones de ahorro tienen una relación compleja con las dinámicas de género dentro de la familia. DOWNLOAD THIS PAPER FROM SSRN: http://ssrn.com/abstract=2371112
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".