Bibliographic record
Abstract
Indigenous* college students in both Canada and the United States have the lowest rates of obtaining postsecondary degrees, and their postsecondary dropout rates are higher than for any other minority (Freeman & Fox, 2005; Mendelson, 2004; Reddy, 1993). There has been very little research done to uncover possible reasons for such low academic achievement and high dropout rates for Indigenous students. Some of the research that has been done indicates that one challenge for Indigenous students is the difficulty in navigating the cultural differences between higher education and their Indigenous cultures. Biculturalism is the ability of an individual to navigate two different cultures (Bell, 1990; Das & Kemp, 1997). Several scholars have suggested that biculturalism is an important construct in understanding academic persistence among Indigenous students (Jackson, Smith & Hill, 2003; Schiller, 1987). This study explored biculturalism among Indigenous college students and how it impacts their higher education experience. Indigenous college students (n=26) from the southwestern United States and central Canada participated in qualitative interviews for the study. The interviews were transcribed and interpreted using a synthesis of qualitative methods. Several themes related to the participants' experience of biculturalism emerged from the qualitative analysis: institutional support for transition to college, racism, types of relationships to native culture, career issues, and family issues. The findings suggested that more needs to be done in terms of providing Indigenous students centers at universities, implementing mentor programs for incoming students, and educating future Indigenous college students, families, and communities about biculturalism and the culture of higher education. *Author's note: The term Indigenous will be used to describe Native American/American Indian, First Nation and Métis student participants. Interviews were collected both in the United States and Canada. The terminology used to describe these populations differs across cultures; therefore, Indigenous will be used as a more general term, to describe the participants. The terminology used by cited authors was retained.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".