The NexSTEM Program: A Community Assets Program that Fosters the Next Generation of STEM Leaders
Bibliographic record
Abstract
Underrepresentation in science, technology, engineering, and mathematics (STEM) fields by individuals with low socioeconomic status (SES) is a long-standing concern. Funded in late 2018, the NexSTEM Program (the “Program”) is a National Science Foundation multi-institution consortia S-STEM grant-funded program at Illinois Wesleyan University (IWU), Illinois State University (ISU), and Heartland Community College (HCC) with the goal of reducing barriers for low SES students from Central Illinois enrolling in and completing STEM degree programs at the three institutions and identifying effective, and sustainable, programmatic components. To help address barriers, the Program awards 2- and 4-year scholarships to academically successful students with significant financial need, and pairs the scholarships with multi-level mentoring, academic supports, and hands-on STEM research project involvement beginning in the first semester of college for both 2-year and 4-year students. Uniquely, HCC students who want to transfer to IWU or ISU can take their scholarship with them as they complete their 4-year STEM degree. The Program has now onboarded 2 cohorts of largely Pell-eligible first year students pursuing an eligible STEM major at one of the three IHEs. This presentation will discuss program structure, interim outcomes related to the current cohorts, and implications for the efficacy of this model in improving retention and representation in STEM.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".