Exoplanets in the Classroom: A Bilingual K12 Educational Suite for Exploring Exoplanet Science
Bibliographic record
Abstract
Abstract A groundbreaking collaboration in Canada has united astrophysicists, science educators, and teachers to create Exoplanets in the Classroom – a dynamic suite of K-12 resources designed to inspire the next generation of scientists. Featuring hands-on activities, practical slide decks, engaging videos, and profiles of trailblazing Canadian astronomers, this comprehensive collection of resources is freely accessible online in both French and English. Since 2021, the Trottier Institute for Research on Exoplanets (IREx) at the Université de Montréal, in partnership with Discover the Universe and other educational innovators, has crafted these resources with input from over one hundred Canadian teachers. This rigorous, iterative process ensures seamless integration into a wide range of subjects, from science to the arts, all while meeting Canadian K-12 curriculum standards. These innovative resources provide educators with the tools to captivate students with the wonders of exoplanet research and the stories of diverse, local scientists at the forefront of discovery. Already tested and embraced by students and teachers from diverse backgrounds, these materials are now poised to inspire a global audience, offering astrophysicists and educators a powerful way to ignite curiosity and engage learners in classrooms and beyond.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.012 |
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".