Exploring Factors that Predict STEM Persistence at a Large, Public Research University
Bibliographic record
Abstract
The present study explores demographics, pre-college characteristics and multi-year (2003-2013) tracking of a census of 53,077 students who initially declared a STEM major upon entering a research university in Texas and seeks to predict graduation with a STEM and non-STEM degree. Guided by QuantCrit theory, we use multilevel models to determine factors that predicted persistence in any major and factors that predicted persistence in STEM, as well as use marginal effects to explore the intersection of ethnicity, sex, and first-generation status. Results highlight the disparity that exist amongst Black students and their White counterparts with regards to persistence in any major. We also highlight the gap between first-generation White and Black first-generation females and their Asian and International counterparts with regards to persistence in STEM. Implications for future research and practitioners suggest further attention needs to be paid to Black first-generation students.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".