Research Collaboration Creates Opportunities for High-Impact Undergraduate Research
Bibliographic record
Abstract
ON THE WEBmight lead participants to feel isolated, the PIs and faculty at the host institutions implemented several measures: 1.A mechanism was put in place that enabled the REU participants to regularly communicate with one another through Canvas.2. Ice-breaker and collaborative/competitive activities were conducted throughout the summer at the host sites.3. As noted, a final face-to-face meeting was hosted at CWU, and field trips were funded using non-NSF funds from CURPA and CWU.4. The PIs communicated at least weekly with the REU students, providing program updates and feedback.Evidence that these measures were effective in addressing isolation can be found in the students' exit surveys.Asked what elements of the REU had the greatest impact or benefit, the fourteen participants of the program listed: "Social interactions with Mentor at the Host Institution" (mentioned 12 times), "Social interactions with Students at the Host Institution" (9 times), and "End-of-the-summer activity at Central Washington University" (9 times).One student described her interactions with the REU faculty and students as the "best part of the research experience."Seven of the fourteen participants entered the summer research interested in obtaining a PhD.By the end of the summer, 11 of the 14 students (79 percent) indicated such an interest.Although the desire to pursue a doctoral degree is neither the focus of our program nor necessarily an indicator of its success, it is indicative of students' increased confidence in their ability to perform research.It also suggests that a distributed REU is as effective in building students' confidence in their ability to do graduate-level work as is the more traditional single-campus REU.
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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 | 0.131 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.004 | 0.041 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".