Identifying communication strategies employed by informal learning experiences that are predictive of climate action intentions
Bibliographic record
Abstract
Effective climate change education is needed to drive immediate collective action, which will be necessary to ensure a habitable planet for future generations. In response to this identified need, an abundance of research on climate change education exists. However, the bulk of this research focuses on formal in-class educational initiatives and operationalizes their success based on how much participants learn, and not whether they feel motivated and prepared to combat climate change. The current study sought to remedy these gaps in the literature by investigating the role of informal learning environments in sparking climate action. Specifically, the study surveyed visitors to the Climate Action Show, an interactive and immersive climate science exhibit at Science North in Sudbury, Ontario, to determine which characteristics of the experience motivated them to take climate action. Respondents identified twelve different characteristics of the show as having influenced them to take action, some of which are exclusive to, or more commonplace within, informal learning environments. Findings suggest that informal learning environments represent a promising avenue for actionable climate change education and provide a foundational understanding of which characteristics visitors to these environments self-report as motivating action.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".