Place and transformative learning in climate change focused community science
Bibliographic record
Abstract
Community science involves the co-creation of scientific pursuits, learning, and outcomes and is presented as a transformative practice for community engagement and environmental governance. Emphasizing critical reflection, this study adopts Mezirow’s conception of transformative learning to theorize the transformative capacity of community science. Findings from interviews with participants in a community science program reveal critical reflection, although instances acknowledging attitudes and beliefs without challenging personal assumptions were more common. Program elements most likely to prompt participants to identify beliefs, values, and assumptions include data collection and interaction in team dynamics, whereas data collection in a novel environment was most likely to prompt participants to challenge their beliefs, values, and assumptions. A review of 71 climate change focused programs further demonstrates the extent that program designs support transformative learning. Key features of the community science landscape like the broad inclusion of stated learning objectives offer a constructive starting point for deepening transformative capacity, while the dominance of contributory program designs stands as a likely roadblock. Overall, this study contributes by applying a developed field to theorize transformation in relation to community science and by highlighting where facilitators should focus program design efforts to better promote transformation toward environmental sustainability.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".