Catalyzing Collaborative Science: Empowering Undergraduate Immunologists for Cross-disciplinary Success 4024
Bibliographic record
Abstract
Abstract Description The evolving workplace increasingly demands effective collaboration across diverse, cross-disciplinary teams to tackle global challenges. This is particularly relevant in immunology, which intersects biology, engineering, and medicine. Immunologists must learn to collaborate with experts from various fields, including biomedical engineering and statistics, yet undergraduate life sciences programs offer limited opportunities for such interactions. Most team projects involve only peers within the same discipline, with few chances to collaborate with students from other institutions. To address this gap, we developed a hybrid pedagogical model that fosters collaboration between undergraduate students at the University of Toronto’s Department of Immunology and the University of British Columbia’s School of Biomedical Engineering. This model simulates real-world immunoengineering collaborations, promoting skills in teamwork, conflict resolution, and project management. In a Scholarship of Teaching and Learning (SoTL) study, we surveyed students’ attitudes towards cross-disciplinary collaboration and assessed their skill development over the course. Here, we detail our scaffolded approach to encourage collaboration in a cross-disciplinary, cross-institutional project and share insights from our SoTL study. Our findings aim to inform future course offerings and training initiatives, ultimately preparing immunologists for effective cross-disciplinary collaboration. Topic Categories Immunology Education and Communication (EDU)
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".