Language justice as an antiracism institutional transformation: Institutional facilitators and barriers for community-engaged cardiometabolic health promotion research
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
This article describes lessons learned from the incorporation of language justice as an antiracism praxis for an academic Center addressing cardiometabolic inequities. Drawing from a thematic analysis of notes and discussions from the Center's community engagement core, we present lessons learned from three examples of language justice: inclusion of bilingual team members, community mini-grants, and centering community in community-academic meetings. Facilitating strategies included preparing and reviewing materials in advance for interpretation/translation, live simultaneous interpretation for bilingual spaces, and in-language documents. Barriers included: time commitment and expenses, slow organizational shifts to collectively practice language justice, and institutional-level administrative hurdles beyond the community engagement core's influence. Strengthening language justice means integrating language justice institutionally and into all research processes; dedicating time and processes to learn about and practice language justice; equitably funding language justice within research budgets; equitably engaging bilingual, bicultural staff and language justice practitioners; and creating processes for language justice in written and oral research and collaborative activities. Language justice is not optional and necessitates buy-in, leadership, and support of community engagement cores, Center leadership, university administrators, and funders. We discuss implications for systems and policy change to advance language justice in research to promote health equity.
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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 | 0.051 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 it