Enhancing Parks and Protected Area Management in North America in an Era of Rapid Climate Change through Integrated Social Science
Bibliographic record
Abstract
The contributions of the social sciences to the advancement of protected areas and climate change adaptation discourses and deliberations have remained relatively marginal and under-recognized. Given the slow response by protected area agencies in terms of the development and implementation of adaption strategies and site-level management actions, it is becoming increasingly clear that solutions to the complex challenges posed by rapid climate change will require an integrated approach—one that extends beyond the biological realm to one that acknowledges the inextricable links between biological and social systems. This paper illustrates how some of the significant advances in the social sciences are improving the cultivation of knowledge of climate change impacts, and reframing protected areas policy and practice. First, we discuss the ways in which social science work has already improved conservation knowledge and practice related to climate change. We argue that social science's critique of conservation ideas and management norms has improved both knowledge and understanding of climate change, understanding the management implications thereof, and has contributed to the development of a range of insightful tools and methods in support of adaptation efforts. We then proceed to outline ways in which the social sciences can be used to communicate uncertain climate risks to policy- and decision-makers, and an increasingly concerned public. Next, we look at emerging governance paradigms relevant to protected area management, including adaptive co-management, which may encourage dialogue amongst stakeholders working in complex, multi-jurisdictional land use planning contexts and enhance management flexibility in an uncertain future. We conclude by emphasizing that the ability of the conservation community to better understand and effectively adapt to climate change will require a more substantive effort to integrate natural and social science perspectives in research, policy and practice. Such adaptations will not be easy and imply a major paradigm shift in current parks and protected areas policy and planning, and the practice of climate change research.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".