First Nations People: Addressing the Relationships between Under-Enrollment in Medical Education, STEM Education, and Health in the United States
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
In the United States of America, an analysis of enrollment statistics to institutions of higher education, those pursuing Science, Technology, Engineering, and Mathematics (STEM) fields, as well as those pursuing medical education show a paralleled ethnic stratification. Based upon such stratification, Native Americans consistently rank amongst the lowest demographic groups to enroll in and pursue higher education, STEM or medical education. A perturbed history of the First Nations people in the establishment of the United States of America laid the foundation for a multitude of factors contributing to current trends in health, living, and academic pursuits amongst First Nation’s people. This paper aims to explore the factors underlying the lack of Native American enrollment in higher education, careers in STEM and medicine. An investigation was conducted following a broad literature review relevant to the topic, and articles were critically appraised using the Search, Appraisal, Synthesis of Analysis (SALSA) framework as well as the Standards for Reporting Qualitative Research (SRQR). Findings from such studies indicate that the Native American communities face a unique set of social circumstances rooted in a historical context, with several unmet basic needs of living required for integration, access, and pursuit of higher education.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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