Female Labor Force Participation and Dependency Ratios in Border States
Bibliographic record
Abstract
Researchers have identified some of the factors affecting female labor force participation (FLFP) as: economic dependency, income inequality, family structure, and fertility, among others. This paper will study the impact of dependency ratios on female labor force participation in the US Border States with Mexico (Texas, New Mexico, Arizona, and California). We use dependency ratios to measure the role of females’ as economic producers and active members of the labor force. The most important finding was that changes in population age structure, affects the female labor force participation rate in the Border States. By following Kelley's approach on dependency ratios, we found that children have a negative effect on the female labor force participation rate. The constant care and attention required by children seems to impede females’ participation in the labor force. However, the elderly population was found to have a positive effect on female labor force participation rate, which could suggest that the elder's involvement in the household promotes female economic activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".