The sustainability of health economics: Proceedings from the 2022 Inter-University Big Data Challenge
Bibliographic record
Abstract
STEM Fellowship’s Inter-University Big Data Challenge is a unique Big Data inquiry and experiential learning program that provides university students worldwide an opportunity to apply computational thinking in search of national, regional, community, and individual health solutions. It is a new form of R&D talent development and identification through computational science and scholarly communication demonstrated by students. As part of the program, participants were offered a broad range of workshops in data analytics, programming, and science communication. Some of the tools the students learned and used include Python, R, machine learning, LaTeX, and Overleaf. This year, the program participants explored issues of The Sustainability of Health Economics and suggested a whole spectrum of original Open Data-based ideas and solutions. Presented research topics are ranging from Improved Health Resource Allocation and Tracking the Spread of a Virus to Health Insurance based on Health Behaviours, and more. Overall, we received submissions from student teams from practically all leading Canadian universities, mixed teams of students from Canada and the US, Asian, and Latin American universities. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the program and wish them the best for their future academic and professional endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without generous support of our sponsors: Hoffman La Roche Canada, Canadian Science Publishing, and JMIR Publications.
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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 | 0.046 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 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".