A model for training undergraduate students in collaborative science
Bibliographic record
Abstract
Engagement of undergraduate students in research has been demonstrated to correlate with improved academic performance and retention. Research experience confers many benefits on participants, particularly foundational skills necessary for graduate school and careers in scientific disciplines. Undergraduate curricula often do not adequately develop collaborative skills that are becoming increasingly useful in many workplaces and research settings. Here, we describe a pilot program that engages undergraduates in research and incorporates learning objectives designed to develop and enhance collaborative techniques and skills in team science that are not typical outcomes of the undergraduate research experience. We conducted a collaborative science project that engaged faculty advisors and upper year undergraduates at four institutions and conducted a review to assess the program’s efficacy. Students developed a broad suite of competencies related to collaborative science, above and beyond the experience of completing individual projects. This model also affords distinct advantages to faculty advisors, including the capacity of the network to collect and synthesize data from different regions. The model for training students to conduct collaborative science at an early stage of their career is scalable and adaptable to a wide range of fields. We provide recommendations for refining and implementing this model in other contexts.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".