The Challenges of Going Global with Undergraduate Research: The Matariki Undergraduate Research Network
Bibliographic record
Abstract
To determine the logistics, benefits, and challenges of developing and implementing an international undergraduate research network, the authors analyzed the Matariki Undergraduate Research Network (MURN). MURN attempted to connect undergraduate students from four countries across two years, with 21 and 23 students respectively. Using mixed methods, the authors explored faculty and student experiences of MURN. Although MURN worked well at the local level, it had limited success at a global level. Teaching across time zones and academic-year differences posed the biggest challenges. Students and faculty reported a range of benefits typical of engagement with undergraduate research but noted weak international connections. A credit-bearing program with partners in similar time zones and academic-year systems, as well as a requirement for collaborative projects across institutions, are recommended.
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How this classification was reachedexpand
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 | 0.098 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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; both teacher heads agree on what is shown here.
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".