The Socialization and Retention of Low-Income College Students: The Impact of a Wrap-Around Intervention
Bibliographic record
Abstract
The Strategic Undergraduate STEM Talent Acceleration INitiative (SUSTAIN) provided a coherent ecosystem of academic, social, and career support services designed for a diverse cohort of high-achieving, low-income STEM students during their first year of undergraduate study. Findings are discussed in terms of the efficacy of the program interventions to enhance students’ socialization and retention within the STEM community. Results indicate that participants perceived the interventions to have helped them adjust to college life and develop skills in understanding science and the scientific process. Which in turn, participants reported, helped them to succeed in their STEM courses and visualize themselves as part of the larger STEM community. The participants rated STEM faculty mentoring, research experience and community building as more helpful than other interventions. Our findings will aid researchers to better understand how SUSTAIN interventions influence students’ socialization into the STEM community and provide valuable insight to guide policymakers in shaping future programs that are successful in retaining diverse students in STEM fields.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".