The Experiential Learning for Veterans in Assistive Technology and Engineering (ELeVATE) program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it