Harnessing AI and Open Data Analytics to Combat Social Inequities Among Adolescents: 2024-25 High School Big Data and AI Challenge
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,008 | 0,003 |
| Science ouverte | 0,002 | 0,010 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».