MUREs: a new member of the URE-CURE family of research opportunities for undergrads
Bibliographic record
Abstract
Undergraduate research experiences are important for the development of scientific identity, appreciation of authentic research, and improvement of persistence toward science careers. We identified a gap in experiential research opportunities for undergraduate Biology students who were seeking a formal yet small-scale research experience that was unique to their own interests and career aspirations. These opportunities may be especially worthwhile for of science, technology, engineering, and mathematics (STEM) students aspiring to nonresearch scientific careers (i.e., medicine, dentistry, forensics, and communication) and underrepresented STEM students. Here, we reflect on the use of small-scale, individualized undergraduate research experiences that are based on established methods. These experiences have helped to fill this gap and create problem-centered learning opportunities for undergraduate students that are as unique as the students themselves.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".