Overcoming social barriers to learning and engagement with climate change adaptation: experiences with Swedish forestry stakeholders
Bibliographic record
Abstract
Climate change is expected to significantly affect forestry in the coming decades. Thus, it is important to raise awareness of climate-related risks – and opportunities – among forest stakeholders, and engage them in adaptation. However, many social barriers have been shown to hinder adaptation, including perceptions of climate change as irrelevant or not urgent, underestimates of adaptive capacity and lack of trust in climate science. This study looks into how science-based learning experiences can help overcome social barriers to adaptation, and how learning in itself may be hindered by those barriers. The study examines the role of learning in engagement with climate change adaptation with the help of the theory of transformative learning. Our analysis is based on follow-up interviews conducted with 24 Swedish forestry stakeholders who had participated in a series of focus group discussions about climate change impacts and adaptation measures. We find that many stakeholders struggled to form an opinion based on what they perceived as uncertain and contested scientific knowledge. The study concludes that engagement with climate change adaptation can be increased if the scientific knowledge addresses the needs, objectives and aspirations of stakeholders and relates to their previous experiences with climate change and extreme weather events.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".