Before the House Committee on Education and Labor
Bibliographic record
Abstract
Thank you Chairman Miller and Members of the Committee for providing me with the opportunity to speak to you today. My name is Heather Boushey and I am a senior economist at the Center for Economic and Policy Research, a non-partisan think tank in Washington, DC. My area of expertise is the U.S. labor market, with an emphasis on the interconnections between labor, social policy, and work/life balance. The way to strengthen the middle class is to ensure equal pay for women. Most women are in the labor force, including over 70 percent of all mothers. Yet, women continue to earn less than men even if they have similar educational levels and work in similar kinds of jobs. The typical full-time, full-year working woman earns only 77 percent of what her male counterparts make. About 40 percent of this gap in pay cannot be explained by women’s choices. The gender pay gap is not just a women’s issue. This is a pressing issue for middle class families. The typical wife brings home about a third of her families total income. Over the past few decades, families who had a working wife were more likely to be upwardly mobile. Since the late 1970s, the additional earnings of wives has made the difference between falling and
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.115 | 0.085 |
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".