The Effect of Child Care Programs on Women in the United States
Bibliographic record
Abstract
Compared to other developed nations, women in the United States are significantly behind on the global stage in terms of workforce participation and, as a result, economic welfare empowerment and advancement, in part due to the absence of a universal child care system in the United States. The child care crisis has garnered attention from citizens and researchers in recent years due to its multifaceted impact on economic stability, families, childhood development, and workforce participation. This draws the question: how much do each state's child care policies affect women's economic welfare and workforce participation? Although previous research has measured the specific effects of the child care crisis on varying factors, general research regarding the effects of child care services between states in order to determine the impact of child care on workforce discrepancies and economic factors pertaining to women is limited. Furthermore, research concerning these topics tends to group the United States together as a whole, providing general implications regarding the lack of a universal child care system yet failing to provide insight into current child care systems that may be working. Research in this study utilizes four years of off-the-shelf data from all 50 states and the District of Columbia, focusing on variables such as the federal medical assistance percentage and child care eligibility block grant threshold in order to determine how much these factors have an effect on women in the workforce. By undertaking a comparative study, there is potential to unveil specific aspects of child care services in particular states that could offer insights into the correct way to maneuver policy reform.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".