Reclaiming Our Narratives: An Indigenous Evaluation Framework for Urban American Indian/Alaska Native Communities
Bibliographic record
Abstract
Aligning with the Western Door—Do Good Work, this article outlines Urban Indian Health Institute’s (UIHI) Indigenous Evaluation Framework, created to explicitly include and empower urban Indigenous communities to reclaim their narratives by using evaluation as a tool to tell their stories and to build capacity to take ownership of research and evaluation. The framework includes the following core values: Urban Indigenous People Create Communities Wherever They Are, Resilient and Strength-Based, Decolonize Data, and Community Centered. The authors provide an overview of how they applied the framework in collaboration with 18 urban Indian organizations through the UIHI’s community grants program and include a first-hand example of implementation of the framework from the Native American Youth and Family Center, a community grantee. The authors highlight the importance of including urban Indigenous people in evaluation contexts, as evaluation is not just an exercise in methods or logistics but also a political act and an assertion of Indigenous values and sovereignty, one that defines who is counted, how people are counted, and what decisions are made. The UIHI’s Indigenous Evaluation Framework aims to decolonize data to reclaim urban Indigenous narratives from colonial understandings and tell the stories of our communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".