An exploration of parents’ engagement with learning about climate change through science education: learnings for future practice
Bibliographic record
Abstract
Climate change education is crucial to addressing the climate crisis. Studies show that parents and guardians can play an important role in transmitting knowledge, competencies, and a pro-environmental orientation to their children. Whilst climate change education can span many disciplines, it has been effective within science education. In this paper we embrace evidence-based effective approaches to science education, with parents. Climate change education research literature published to date focusses on school-based interventions and there is a dearth of literature on parental involvement. This paper describes the development of, and outcomes associated with, a programme to promote engagement with climate change education for families from socioeconomically disadvantaged backgrounds. Data were gathered through post semi-structured interviews and reflections on posters and photos created and collected throughout the programme. Findings indicate that this approach engaged parents in climate change education with their children and successfully addressed various misconceptions. The programme presents a real opportunity to engage parents in their children’s formal education.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".