Charting the Pandemic with Data Modelling — with Caroline Colijn
Bibliographic record
Abstract
Caroline Colijn works at the interface of mathematics, evolution, infection and public health. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She did her PhD in applied mathematics at the University of Waterloo, where she studied the foundations of quantum mechanics. She changed tack in her postdoctoral years, working on mathematical modelling with Prof. Michael Mackey at McGill and on TB modelling and epidemiology in Megan Murray's group at the Harvard School of Public Health and the Broad Institute at MIT. She moved to the Department of Engineering Mathematics in Bristol, England in 2007 and joined Imperial College London's Department of Mathematics in 2011. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial's Centre for the Mathematics of Precision Healthcare.
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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 | 0.037 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 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".