International High School Big Data Challenge 2018, New York Academy of Sciences
Bibliographic record
Abstract
The New York Academy of Sciences' Big Data: Think Global, Act Local Challenge provided an opportunity for students to experience working with data analytics by combing scientific theories with statistics, programming and innovation.Students analyzed large data sets provided by the STEM Fellowship and other open access sets to reveal patterns and trends related to human behavior and interactions.98 students participated from 29 countries who formed 26 teams that were overseen by 20 participating mentors.The teams were invited to address one of three issues: the correlation between food and climate change, renewable energy and sustainable infrastructure, or climate change and the economy.They investigated how big data can shed light on these issues and whether the connections made influence the future on both a government and individual level.They were encouraged to use a variety of data analysis algorithms, including classification algorithms, regression, network analysis text analysis and association rules.Each teams' solutions were judged on a variety of criteria which included: how innovative their solution was, if the concept was clear and concise, if the analysis showed potential to make a difference and how, , potential social impact, and if the experience was a collaborative endeavor.The Big Data Challenge was sponsored by Medidata, Regeneron and S&P Global.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.063 | 0.025 |
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".