Social Studies, Science, and Civics: Teacher Education and Citizen Science in the 21st Century
Bibliographic record
Abstract
Citizen science, research in which members of the public actively contribute scientific data, has recently evolved as a means to support scientific inquiry in the classroom, particularly in fields related to ecology and environmental science. Our research focuses on a collaborative project with teacher candidates, a science education professor, and a social studies education professor at a Canadian university. Teacher candidates were engaged in the classroom and beyond as they explored topics related to civics education and evidence-based decision making. Our findings demonstrate the potential effectiveness of a citizen science lens for science, social studies, and generalist teachers. Key Words: citizen science, citizenship/civics education, science education, social studies education, teacher education La science citoyenne, c'est-à-dire la recherche dans laquelle les membres du public contribuent activement aux données scientifiques, a récemment évolué comme un moyen de soutenir la recherche scientifique en classe, en particulier dans les domaines liés à l'écologie et aux sciences de l'environnement. Notre recherche porte sur un projet de collaboration avec de futurs enseignants, un professeur d'enseignement des sciences et un professeur d'enseignement des sciences sociales dans une université canadienne. Les candidats à l'enseignement se sont engagés dans la salle de classe et au-delà en explorant des sujets liés à l'éducation civique et à la prise de décision fondée sur des preuves. Nos résultats démontrent l'efficacité potentielle d'une optique de science citoyenne pour les professeurs de sciences, d'études sociales et les enseignants généralistes. Mots clés : science citoyenne; éducation à la citoyenneté/civique; enseignement des sciences; enseignement des études sociales; formation des enseignants
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.032 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".