Similarities and Differences in Barriers and Opportunities Affecting Climate Change Adaptation Action in Four North American Landscapes
Bibliographic record
Abstract
Climate change presents a complex set of challenges for natural resource managers across North America. Despite recognition that climate change poses serious threats to species, ecosystems, and human communities, implementation of adaptation measures is not yet happening on a broad scale. Among different regions, a range of climate change trajectories, varying political contexts, and diverse social and ecological systems generate a myriad of factors that can affect progress on climate change adaptation implementation. In order to understand the general versus site-specific nature of barriers and opportunities influencing implementation, we surveyed and interviewed practitioners, decision-makers, and scientists involved in natural resource management in four different North American regions, northern Ontario (Canada), the Adirondack State Park (US), Arctic Alaska (US), and the Transboundary Rocky Mountains (US and Canada). Common barriers among regions related to a lack of political support and financial resources, as well as challenges related to translating complex and interacting effects of climate change into management actions. Opportunities shared among regions related to collaboration, funding, and the presence of strong leadership. These commonalities indicate the importance of cross-site learning about ways to leverage opportunities and address adaptation barriers; however, regional variations also suggest that adaptation efforts will need to be tailored to fit specific ecological, political, social and economic contexts. Comparative findings on the similarities and differences in barriers and opportunities, as well as rankings of barriers and opportunities by region, offers important contextual insights into how to further refine efforts to advance adaptation actions in those regions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".