Why Don’t More American Indians Become Engineers in South Dakota?
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".