UC Network on Child Health, Poverty and Public Policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose: Develop a trans-disciplinary network across the UC that produces a more comprehensive understanding of the varied pathways by which early life health disparities influence children’s contemporaneous and long term well being, with an eye towards informing cutting edge policy interventions. Significance: Despite rhetoric around the “American Dream,” numerous metrics suggest that there is less upward mobility in the United States than in most developed countries in the world. For example, a child born to parents in the bottom fifth of the income distribution has a more than 40% chance of remaining there as an adult, and a 65% chance of ending up in the bottom two fifths. In contrast, in Canada and most western European countries, the odds of the same child ending up in the bottom two fifths is closer to 50% (Winship 2011). Studies in economics, psychology, and the biological sciences increasingly suggest that early life health and health environments may play a critical role in reducing children’s chances of escaping poverty and reducing disparities. The UC system includes many noteworthy scholars who are independently contributing to this knowledge base, but narrow, disciplinary specific approaches will not yield the most impactful results. Small steps taken in tandem with multiple disciplines, using multiple approaches, will add up to larger gains in knowledge that can ripple through to create bigger societal influences. We can further speed up these ripple effects by targeting graduate student training. Approach: Year 1: Run transdisciplinary workshop(s) that bring together faculty and graduate students across a wide range of disciplines (e.g. economics, psychology, nutrition, epidemiology) to share disciplinary specific expertise, identify group research strengths, important gaps in knowledge and research questions. Begin to build relationships with policy practitioners to ensure that ensuing research is decision-relevant. Year 2: Support 1-4 network projects that prioritize graduate student training, continue to build trans-disciplinary knowledge and stakeholder relationships through in-person meetings that also expand networks across the UC. Submit for a MRPI award and other funding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.013 |
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 it