Teaching about Climate Change: Making global climate change meaningful to K-8 students
Bibliographic record
Abstract
Given the pace of the Earth’s warming, today’s school children are expected to feel its effects more than any previous generation (Plattner, Gian-Kasper, 2013). Education is thought to play a vital role in preparing today’s youth for meeting the challenge (Kegawa & Selby, 2010). This study takes a qualitative look at how a sample of K-8 teachers in Ontario, Canada are creating meaningful and engaging opportunities for students to learn both about the complexity of climate change and about their own agency in responding to it. Two semi-structured interviews were conducted to gather the data which was analyzed and presented in this report. The interviews uncovered that K-8 educators are well positioned to address the multiple dimensions of the phenomenon as they usually teach their students multiple subjects. Secondly, the participants are relating the content to the lives of their students and are encouraging independent thought. As a perceived consequence of their pedagogy, they observed increased engagement and thoughtfulness about the environment the more they learned about it. The study also found that both the physical landscape and school community play a role in supporting teaching about climate change. Finally, the decision to include the topic was made independently by the participants rather than by curriculum mandate. The implications of the findings are then discussed in the context of the existing literature on the topic as well as the educational community. The findings are used to offer some suggestions for the future of climate change education as well as areas for further research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".