Indigenous Knowledge Realized: Understanding the Role of Service Learning at the Intersection of Being a Mentor and a College-Going American Indian
Bibliographic record
Abstract
The article explores the experiences of 13 undergraduate American Indian college students who served as mentors through a service-learning course while attending a 4-year, predominantly White institution (PWI). This chapter elucidates how serving as a mentor allowed participants to draw on three culturally-relevant persistence factors in higher education: relationship, community, and power. Previous research demonstrates that service learning actively involves college students and encourages them to build a connection and a sense of commitment to the community (Lee & Espino, 2011; Rhoads, 1998). Through a Tribal Critical Race Theory lens, the purpose and function of service learning is deconstructed and redefined to fit the needs of North American Indigenous college students. This article reveals that Indigenous undergraduate students tapped into their own supply of Indigenous knowledge in relating their mentoring experience to building meaningful relationships, to being a positive influence in tribal communities, and to recognizing that service is a cyclical power that positively impacts their collective role in society. The article details how relationship, community, and power from Indigenous perspectives are sources of persistence for American Indian students and how social justice-based, service-learning courses provide safe spaces for students to realize their Indigenous knowledge while attending PWIs.Keywords: American Indian college student; service learning; Indigenous knowledge>
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".