Retention of Underrepresented Minority Undergraduates in STEM
Bibliographic record
Abstract
Far fewer undergraduate students pursue and complete STEM degrees compared to humanities degrees, despite high demand for STEM professionals. Among undergraduate STEM majors, individuals from underrepresented racial minority (URM) groups are far less likely to complete their degree than their White or Asian peers, presenting a serious obstacle to diversity within the STEM workforce. Drawing from Bandura’s Social Cognitive Theory, researchers have identified factors that affect the retention of URM students in STEM, though there is substantial evidence that such factors are moderated by environmental influences not traditionally included in the theory. In this paper, we argue that many environmental influences can be conceptually unified under the State Authenticity as Fit to Environment (SAFE) model. Further, we review literature suggesting that the constructs of both Social Cognitive Theory and the SAFE model interact extensively when considering retention of URM undergraduates, arguing that understanding the interactions between the two models will provide a more complete picture of how retention of URM students can be improved.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".