Indigenous Teacher Education Is Nation Building: Reflections of Capacity Building and Capacity Strengthening in Idaho
Bibliographic record
Abstract
This article discusses the efforts of the Indigenous Knowledge for Effective Education Program (IKEEP), at the University of Idaho, a predominately white institution (PWI) of higher education, and its struggle to create space in higher education for intentional support of Indigenous self-determination, sovereignty, and Tribal nation building through the preparation of Indigenous teachers. In doing so, we examine the contentious and local work of reimagining education, from the bottom up and top down, to develop leaders to serve the needs of Indigenous youth and communities through the vehicle of mainstream institutions. With data from a multiyear ethnographic documentation, we examine the experiences of IKEEP program administration, teacher mentors, and students through the conceptual lens of Tribal nation building in higher education. Our findings underscore how teacher education programs at PWIs need to engage in a radical shift toward seeing Indigenous teachers as nation builders and to prioritize the infrastructure and programmatic collaboration to support them and their communities as such.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.048 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.009 |
| 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".