ELEVATE-US-UP: Designing and Implementing a Transformative Teaching Model for Underrepresented and Underserved Communities in New Mexico and Beyond
Bibliographic record
Abstract
This paper presents the development, implementation, and outcomes of the ELEVATE-US-UP (Engaging Learners through Exploration of Visionary Academic Thought and Empowerment in UnderServed and UnderPrivileged communities) teaching methodology, an equity-centered, culturally responsive pedagogical framework designed to enhance student engagement, academic performance, and science identity among underrepresented learners. This framework was piloted at Northern New Mexico College (NNMC), a Hispanic- and minority-serving rural institution. ELEVATE-US-UP reimagines science education as a dynamic, inquiry-driven, and contextually grounded process that embeds visionary scientific themes, community relevance, trauma-informed mentoring, and authentic assessment into everyday instruction. Drawing from culturally sustaining pedagogy, experiential learning, and action teaching, the methodology positions students not as passive recipients of content but as knowledge-holders and civic actors. Implemented across upper-level environmental science courses, the method produced measurable gains: class attendance rose from 67% to 93%, average final grades improved significantly, and over two-thirds of students reported a stronger science identity and a newfound confidence in their academic potential. Qualitative feedback highlighted increased perceptions of classroom inclusivity, community relevance, and instructor support. By centering on cultural context, student voice, and place-based application, the ELEVATE-US-UP framework offers a replicable and scalable model for educational transformation in underserved regions.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".