Bibliographic record
Abstract
Within the chapter, a high school Science educator in Ontario, Canada reflects upon climate change education since 2008 when a new science curriculum was introduced. The chapter highlights how shifting student and societal perspectives, along with changes in the political landscape, have generated a rapid evolution in climate education. This evolution has led to classroom practices struggling to keep pace with the accelerating climate crisis and the anxiety stemming from it. Educators now face the dual challenge of addressing climate change within the curriculum while helping students navigate the issue psychologically, socially, and academically. The author describes four strategies that underpin her practice: spiralling themes of ecosystems, climate change, and climate justice in science education planning; taking classes outdoors whereby students can connect with nature, understand its cycles, and improve mindfulness; teaching students to develop a growth mindset as a way to face setbacks and to embrace optimism and hope; and most importantly, using a solutions-focused approach such that students can become confident, resilient, critical thinkers, equipped with tangible solutions at every level - from the individual to the global community. Finally, the author provides informal student feedback after participating in her science programmes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.122 | 0.065 |
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".