Harnessing AI and Open Data Analytics to Combat Social Inequities Among Adolescents: 2024-25 High School Big Data and AI Challenge
Bibliographic record
Abstract
The STEM Fellowship High School Big Data and AI Challenge provides students with a unique opportunity to utilize Open Data to investigate one of the UN Sustainable Development Goals while learning data science fundamentals in an experiential learning format – an essential skill set for a young researcher in the digital age. This year students tackled the challenge of Harnessing AI and Open Data Analytics to Combat Social Inequities Among Adolescents. Students suggested their own evidence-based solutions following the principles of Open Science. They investigated different topics, ranging from Optimizing Educational Equity to Advancing Equity for Disabled Youth. These future leaders were tasked with using Open Data to enhance our understanding of social inequities and explore areas for innovation to close inequity gaps and propagate the social pursuit of prosperity for all. Various topics were investigated, identifying different forms of socioeconomic determinants which impact inequity among adolescents on a global scale. By applying computational thinking, students explored the interplay between adolescent inequities and external factors, ultimately contributing to the development of new educational and social development approaches. STEM Fellowship has designed an interdisciplinary, agile educational environment with in-depth learning modules for students as a means to bridge the gap between traditional high school courseware and computational inquiry. Students learned how to uncover hidden patterns and trends in structured and unstructured data using a range of data analytics tools and programming languages. Python, R, LaTeX, and machine learning were some of the tools the students learned and used throughout the program. Additionally, all participants prepared a short slideshow and presented their research to a group of their peers. We are privileged to witness the analytical capabilities of this talented generation of students, and we are confident that they will demonstrate excellence throughout their academic and professional careers. The Western Canada and Eastern Canada finalist events were the culmination of the top participants’ trailblazing research, and were held at the Hunter Hub for Entrepreneurial Thinking at the University of Calgary in Calgary and at Microsoft Canadian Headquarters in Toronto respectively. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge and wish them the best for all of their future endeavours. We also want to express our appreciation to all of the STEM Fellowship volunteers who made this challenge possible. We greatly appreciate the patronage of the program by the Canadian Commission for UNESCO, as well as the Lieutenant Governor of Alberta and the Lieutenant Governor of Ontario. We want to thank Canadian Science Publishing, Environment and Climate Change Canada, Let’s Talk Science, National Research Council Canada, RBC Future Launch, SciNet at the University of Toronto, Hunter Hub at the University of Calgary, Microsoft Canada, Canadian Science Publishing, Overleaf, and Cisco Academy for their invaluable support.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".