Undergraduate Students as Visiting Students in the United Kingdom
Bibliographic record
Abstract
In this work-in-progress paper, we discuss our NSF-supported program designed to select, mentor, and send U.S. undergraduate students in electrical power engineering to the University of Strathclyde in the U.K. during summer to engage in research projects and research-related activities.We discuss the program need, logistics, design, and evaluation results.Each year, six new students participate in the program; they are selected via a nation-wide competition.Our primary motivation for this program is to provide students with experience in international research and help prepare the next generation of U.S. competitive STEM workforce capable of innovation.Moreover, the students will develop soft skills such as teamwork, oral and written communication, and time management.Since the operating parameters of the electric grid (e.g., frequency and voltage levels) are different in Europe from those in North America, the students will also gain a firsthand experience of different practices in distribution and transmission systems.The research performed under this program helps achieve the smart grid vision through a combination of technological advances and workforce training.Ultimately, this research will increase the utilization of smart grid infrastructure by integrating distributed renewable energy resources.Our preliminary evaluation results show that overall student participants were very satisfied with how the program is set up, designed, and run, giving an average score of 4.7 out of 5: 100% of scholars said they would consider graduate school after attending this program, and pointed to its catalyzing role.Students appreciated the international experience; for all of them, it was their first time living in a foreign country for an extended time.For more than half, this was also the first time to Europe; 90% of scholars said knowing what they know now, they would participate in the program again, citing hands-on research experience, learning about the culture, learning how others solve their power needs, and availability of PhD students to help them as the highlights of the program.We have received applications from 17 states, and 8 of 16 scholars were female, providing evidence for the effectiveness of our advertisement and minority recruitment plans.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.101 | 0.025 |
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".