2021 Undergraduate Big Data Challenge: Infodemiology for the future of digital and public health
Bibliographic record
Abstract
STEM Fellowship’s Undergraduate Big Data Challenge (UnBDC) is an inquiry-driven and experiential learning program that invites students from across the country to strengthen their problem-solving and critical thinking skills while gaining familiarity with the fundamentals of data science. By allowing students to undertake independent research projects that tackle real-world public health and bioinformatics problems, the BDC fosters scientific inquiry and prompts new and innovative ideas. This year, we invited students to investigate the theme of Infodemiology for the Future of Digital and Public Health. It allowed students to explore practical applications and insights of infodemiology to discover breakthrough connections in Digital and Public Health using open social, demographic, and health data. Students explored many topics, ranging from using Twitter machine learning for fighting the COVID-19 infodemic, to the sentiment comparison between real and fake COVID-19 news articles. We developed in-depth learning modules designed to lead the student from zero-knowledge to an elementary working proficiency in data science. The students learn a broad range of data analytics tools, methods and programming languages which are useful for uncovering hidden patterns, trends in structured and unstructured data. Some of the skills the students learnt and used includes Data Visualization, Classification, Statistics and Data Handling, Overleaf etc. On behalf of STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. It has been a privilege for us to witness the analytical capabilities of the next generation of students firsthand, and we are certain all entrants will continue to demonstrate excellence in their respective careers.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.049 | 0.021 |
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".