Cultivating a Space for Intergenerational Directed Research Groups for Indigenous Students and Allies through Indigenous Knowledge Families
Bibliographic record
Abstract
Abstract: Colonialism has had direct impacts on the transmission of American Indian and Alaska Native (AIAN) knowledge systems. Spaces of higher education continue to create challenges for Indigenous students, especially through the disconnection between Native knowledges and the dominant knowledge being taught. These challenges are reflected in the educational attainment disparities between AIAN and non-Indigenous students in the United States and Canada. To address these issues, we emphasize the significance of promoting ethical approaches to Indigenous research and embracing Indigenous ways of knowing. We examine how combining a directed research group (DRG) with a Knowledge Family approach can shift Indigenous experiences with higher education knowledge production. We describe the structure and goals of the DRG Knowledge Families program, which provides support and resources for Indigenous students while fostering meaningful relationships between Indigenous and non-Indigenous researchers. The DRG Knowledge Families approach integrates research opportunities for Indigenous students, focusing on Indigenous knowledge systems and research methodologies. Ultimately this approach aims to create research spaces that value Native knowledge, center community needs, and support the success of Indigenous students.
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 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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".