Using civically engaged research to promote young Black and Latino children’s well-being: lessons from a new interdisciplinary community-university, faculty–student collaboration
Bibliographic record
Abstract
This paper presents how a new interdisciplinary, faculty–student research team collaborates with multiple community partners to develop civically engaged research (CER) addressing young children’s inequitable economic mobility. We aim to create more equitable, culturally responsive prenatal to age five (PN-5) systems of care that promote economic mobility for families in Charlotte, North Carolina and beyond. As a secondary objective, we leverage our CER partnerships and research lab model to create civically engaged learning (CEL) opportunities for diverse students, amplifying community impact. We detail our origins, shaped by a university initiative and two community calls to action, and explain how overcoming CER challenges – developing new projects, managing competing output pressures, and addressing external funding demands – has solidified our PN-5 identity and CER partnership. We share key lessons learned, including the value of strong mentorship in guiding CER development, internal deliberation to navigate interdisciplinary and cross-rank academic pressures, and co-producing materials to maximize impact. We also share strategies for creating outputs that ensure mutual benefit for all – community partners, faculty, and students, and highlight the benefits of integrating CEL opportunities within CER partnerships. Overall, we hope our experiences inspire others to foster meaningful CER partnerships that enhance community well-being.
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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 | 0.035 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.018 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".