A model for cross-disciplinary and cross-institutional student collaboration for undergraduate immunologists
Bibliographic record
Abstract
Abstract Immunology is a cross-disciplinary field, existing at the interface of biology, engineering and medicine. Therefore, a crucial skill for immunologists to develop is their ability to collaborate with others who have vastly different expertise and skillsets, such as biomedical engineers and statisticians. Undergraduate students at the University of Toronto (UofT) have few formal opportunities to engage in these types of collaborations. Most team-based projects allow students to work with fellow immunology peers, or students from other biology disciplines; however, the opportunity to do so with students in other fields is rare. Even fewer opportunities exist to collaborate with students from other academic institutions. To address this gap, two undergraduate courses, IMM360 (Scientific Methods and Research in Immunology, UofT) and BMEG372 (Biomedical Materials and Drug Delivery, University of British Columbia), were knit together in September-December 2022 and 2023. Here, we present our scaffolded approach for promoting collaboration on a cross-disciplinary, cross-institutional project. Furthermore, we will report student attitudes towards cross-disciplinary collaboration, which were assessed via surveys both at the start and end of the collaborative project. We hope that learnings from this work will inform an effort to weave cross-disciplinary collaboration throughout all years of the Immunology undergraduate program at the University of Toronto and other institutions.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".