Effects of Indigenous Epistemology on Indigenous Secondary Retention Rates
Bibliographic record
Abstract
This article presents the findings of a qualitative study that examines how Indigenous epistemology affects secondary Indigenous students’ retention rates within public schools. The purpose of this study was to focus on Indigenous epistemology that is present in Indigenous culture and language courses to determine whether Indigenous students who engage in this curriculum have higher success rates than those of Indigenous students who do not participate in this particular curriculum. As a Blackfoot scholar, I used a Blackfoot theoretical framework grounded in an Indigenous research methodology. Eight Blood Tribe members were interviewed: four participants (three graduates and one non-graduate) who attended a high school with Indigenous epistemology courses (offered Blackfoot language classes and Aboriginal Studies) and four participants (three graduates and one non-graduate) who attended a high school that did not offer Indigenous epistemology courses (did not offer Blackfoot language classes and Aboriginal Studies). The findings show that not only does the epistemology in the school play a role in Indigenous students’ success in public education, but the epistemology also accompanies and influences the participants throughout their adult lives by shaping their identities and affecting how they function as adults.
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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".