The Influence of Persistence Factors on American Indian Graduate Students
Bibliographic record
Abstract
The underrepresentation of American Indian students continues to exist at the undergraduate and graduate levels of postsecondary education despite increases of American Indian student enrollment. The purpose of this quantitative study is to identify correlations between academic factors and graduate student persistence, as well as to understand how likely graduate degree completion is based on known academic factors for American Indian students. The analyses of the data included survey results, descriptive statistics, bivariate correlation, and multivariate regression. A sample of n=63 American Indian Graduate students represented 41 tribes and villages with over 32 unique tribal languages. The respondents indicated a challenge to balance graduate school, family and cultural responsibilities, however most felt a personal responsibility to complete their graduate degrees for their communities.Although academic factors, American Indian programs, and self-awareness are not significant predictors of American Indian Graduate student persistence, the relationship between the independent variables and the dependent variable were statistically significant. Implications for academic institutions include strategic planning with American Indian representation throughout the entire process.Recommendations for future research include further development of measurable concepts of indigenous theories and recognition of dual conclusions for American Indian and non-American Indian researchers.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".