Climate Change in School: Where Does It Fit and How Ready Are We?.
Bibliographic record
Abstract
Research indicates that teachers place a high priority on climate change as a topic their students should know, but report their own knowledge as inadequate for teaching it. Students (and some teachers) seem unable to distinguish among related environmental issues, and treat general “environmentally friendly” behavior as affecting all issues. The curricular fit of global climate change is best in Earth systems oriented classrooms but opportunities exist across the curriculum; instructional materials are available, though these may not address misconceptions. Some interest groups oppose human-mediated climate change as a curriculum topic, for the same reasons they oppose public action on the problem. Resume D’apres la recherche, les enseignants estiment qu’il est important pour leurs eleves d’etre au courant du changement climatique, mais que leur propre connaissance du phenomene n’est pas a la hauteur. Les eleves et certains enseignants semblent incapables de distinguer les enjeux environnementaux connexes et considerent que le comportement ecologique en general affecte tous les enjeux. L’adequation du changement climatique planetaire avec le programme d’etudes est plus reussie dans les cours axes sur les systemes terrestres, mais il existe aussi d’autres possibilites. Des documents d’instruction sont aussi disponibles, quoiqu’ils n’abordent peut-etre pas les interpretations errones. Certains groupes d’interet refusent que le programme d’etudes aborde le sujet du changement climatique cause par les humains pour les memes raisons qu’ils s’opposent a l’action du public face au probleme.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".