Language justice as an antiracism institutional transformation: Institutional facilitators and barriers for community-engaged cardiometabolic health promotion research
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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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 | 0.149 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.046 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".