Climate change, transformative learning, and social action: An exploration of adult climate activists in Manitoba, Canada
Bibliographic record
Abstract
Recently animated by youth campaigns such as #FridaysforFuture, the climate movement reflects the urgency of the climate crisis in the 21st century. While youth climate activists point to the instability of their own future as a key reason for mobilizing, it is not as clear what catalyzing forces are causing adults to join the climate movement. To investigate, this research explores the role of learning as a catalyzing process through which adult activists in Manitoba, Canada, are motivated to take collective action on the climate crisis. As such, this work attempts to address a gap in the transformative learning literature by examining the intersection of learning and action, and works to advance knowledge regarding pathways to “learn our way out” of complex socio-ecological problems (e.g., climate change). Data for this qualitative study was comprised of literature and document review, semi-structured interviews, and a focus group session with climate activists in Manitoba. Key findings included observing how multiple types of learning (formal, nonformal, and experiential) led participants to climate activism, as well as how experiences of grief, loss, death, and/or trauma motivated involvement in the climate movement. In regard to learning outcomes, this research adds context to the instrumental, communicative, transformative, and introspective domains of transformative learning and draws conclusions about the learning-to-action process as one of accumulated awareness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".