Why Don’t More American Indians Become Engineers in South Dakota?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
American Indians are among the most under-represented groups in the engineering profession in the United States. With increasing interest in diversity, educators and engineers seek to understand why. Often overlooked is simply asking enrolled tribal members of prime college age, “Why don’t more American Indians become engineers?” and “What would it take to attract more?” In this study, we asked these questions and invited commentary about what is needed to gain more engineers from the perspectives of enrolled tribal members from South Dakota, with some of the most poverty-stricken reservations in the nation. Overall, results indicated that the effects of poverty and the resulting survival mentality among American Indians divert attention from what are understood to be privileged pursuits such as engineering education. The study’s findings indicated American Indian interviewees perceived the need for consistent attention to the following issues: 1) amelioration of poverty; 2) better understanding of what engineering is and its tribal relevancy; 3) exposure to engineering with an American Indian cultural emphasis in K-12 schools; 4) presence of role-model engineers in their daily lives; 5) encouragement and support from their peers, families, teachers, Elders, and tribal governments to value science, technology, engineering, and mathematics (STEM) education, particularly engineering fields; and (6) the embedded perceptions of math as a barrier to engineering studies.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it