The Experiential Learning for Veterans in Assistive Technology and Engineering (ELeVATE) program
Bibliographic record
Abstract
Experiential Learning for Veterans in Assistive Technology and Engineering, or ELeVATE, is a program to assist wounded, injured, and ill Veterans in transitioning into university science, technology, engineering, and mathematics programs, with a special emphasis on assistive technology and engineering. This paper examines whether the ELeVATE model, by addressing academic preparation, professional development, rehabilitation counselling, and community reintegration, increases the academic success (defined as enrolling and excelling in a plan of study through a post-secondary institution) of transitioning Veterans with disabilities. Post-program surveys completed by seven participants indicated that they were satisfied with the efficacy of the program. Students rated the research paper and oral presentation of research, the networking seminar, and the resume writing workshop as “very helpful.” They found the group meetings with the vocational coordinator, the introduction to adaptive sports seminar, and the poster presentation to be “moderately helpful.” Seventy-one percent of the students indicated that being part of ELeVATE's supportive cohort of Veterans was “very” or “extremely” valuable. They rated the effectiveness of the support they provided to their peers higher than the support they received from their peers. Over time, ELeVATE participants demonstrated increased self-efficacy (via General Self-Efficacy instrument scores) to succeed in STEM and increased engagement in campus life (via National Survey of Student Engagement scores), and ELeVATE's impact even went beyond helping Veterans achieve their academic and personal goals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".