Changing the sources and usage of energy for a better and sustainable future for all: Proceedings from the 2021-2022 High School Big Data Challenge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
STEM Fellowship’s High School Big Data Challenge is an inquiry-driven experiential learning program that provides students an opportunity to learn and apply the fundamentals of data science – a crucial skill set for a young researcher in the digital age – through independent research projects. The COVID-19 pandemic disrupted high school education, at the same time creating a “fertile ground” for interdisciplinary, student-driven STEM education. This year, we invited students to explore issues of Affordable and Clean Energy at the Individual and Community Levels and to suggest their own evidence-based solutions, using Open Data and the principles of Open Science. Students explored many topics, ranging from Greenhouse Gas Emissions of School Buses to Legitimacy of Electric Vehicles to be the Greener Alternative We developed in-depth learning modules designed to bridge the gap between traditional high school courseware and digital reality and computational science. The students learnt a broad range of data analytics tools and programming languages which are useful for uncovering hidden patterns, trends in structured and unstructured data. Some of the tools the students learnt and used include Python, R, LaTeX, and machine learning. 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 their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, Digital Science, Infor, SCWST, CISCO Networking Academy, Canadian Science Publishing, and the University of Calgary Hunter Hub for Entrepreneurial Thinking. It has been a privilege for us to witness the analytical capabilities of the data-native generation of students first hand, and we are certain all entrants will continue to demonstrate excellence in their respective academic and professional careers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it