An innovative framework for teaching climate change: integrating emotion coping strategies and exploring a new climate emotion scale among Canadian youth
Bibliographic record
Abstract
This study aimed to (a) develop and evaluate an innovative framework for teaching climate change that integrates climate emotion coping strategies, and (b) explore a new climate emotion scale for youth. In phase 1 (pre-intervention), 146 students (aged 11 to 14) from a Canadian school completed the Climate Change Hope Scale and the new Climate Emotion Scale for Youth. In phase 2 (post-intervention), 93 students completed the same questionnaires. The findings identified three categories of climate emotions: positive emotions, negative emotions, and emotional detachment. While positive and negative emotions positively correlated, emotional detachment showed no correlation. Negative and positive emotions were positively correlated with both personal and collective willpower/waypower. Positive emotions exhibited the highest scores, followed by negative emotions, with neither showing significant increases post-intervention. However, emotional detachment did increase significantly, albeit with a small effect size and lower overall scores. Practical implications and future research directions are discussed.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".