Understanding Postsecondary Persistence Based on the Perspective of the Lived Experiences of Alaska Native Students
Bibliographic record
Abstract
This applied dissertation was designed to better understand the factors that influence postsecondary persistence among Alaska Native students. Alaska Native students continue to have the lowest college persistence rate in the country (Benjamin et al., 1993; Brayboy et al., 2012; Bull, 2024; Guillory & Wolverton, 2008; Larimore & McClellan, 2005; National Center for Education Statistics, 2018; National Student Clearinghouse Research Center, 2022; Patterson Silver Wolf et al., 2021; Tachine et al., 2017). Alaska ranks 50th in the country when it comes to the number of students (33%) who enroll and graduate college within 6 years (Alaska Commission on Postsecondary Education, 2023; National Center for Education Statistics, 2024). It is a priority to increase college persistence for Alaska Native students, and initiatives have been implemented in Alaska high schools and universities across Alaska, as well as tribal colleges across the United States, yet Alaska Native students continue to lag far behind their peers when it comes to persisting in college. In this qualitative study, several factors that have an influence on postsecondary education persistence for Alaska Native students were identified. These factors include teachers, the opportunity to take courses that involve career research and postsecondary planning, college readiness, the ability to adapt to a college culture, having a sense of belonging, having adequate counseling support, access to tutoring services both at the high school and college level, and the ability to navigate higher education landscape including processes such as financial aid.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".