What can big data tell us about past, current, and future patterns of infection?
Bibliographic record
Abstract
STEM Fellowship's Big Data Challenge is a unique pedagogical experiment, providing an inquiry and learning experience for high school students that, upon equipping them with top-notch analytical tools, tasks them to find hidden patterns and trends in complex socioeconomic or scientific data.In May 2017, this challenge format was applied for the first time at the undergraduate level, providing teams of university students with the opportunity to delve into datasets related to public health and epidemiology in order to make useful inferences about health and disease.Published here are the abstracts from all entrants.Teams employed a variety of approaches in their respective projects, whether to look at disease from a strictly socioeconomic angle or to examine the efficacy of preventative measures.For all the variation between project themes, it remains that all submissions are of incredibly high quality.Every paper is demonstrative of immense creativity and high potential on the respective team's part.On behalf of STEM Fellowship, I would like to extend my heartfelt congratulations to all students who participated in the challenge, and I wish them all the best for their future endeavours in research and data science.It has been a privilege for us to witness the analytical capabilities of the next generation of students firsthand, and I am certain all entrants will only continue to demonstrate excellence in their respective research careers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".