Understanding perceptions of climate vulnerability to inform more effective adaptation in coastal communities
Bibliographic record
Abstract
Coastal social-ecological systems are vulnerable to climate change with impacts distributed unequally amongst human communities. Vulnerability assessments, an increasingly popular methodology for understanding variability in vulnerability and its components, often fail to include or recognize the perceptions of individuals in the focal system. Perceptions of climate vulnerability are influenced by experiences, social networks, and cognitive biases, and often differ from vulnerability as measured by subject experts. Because perceptions influence human behavior, including if and how people take adaptive action, a failure to recognize perceptions can lead to ineffective adaptation plans and an incomplete understanding of system vulnerability. Here, as part of a novel, multi-method effort to evaluate vulnerability to climate change in the California Current social-ecological system, we survey fishers from Washington, Oregon, and California to understand their perceived vulnerability and investigate what factors drive variability in their views. We find that while there is a connection between some factors known to influence vulnerability of fishers, including vessel size and the diversity of fishing portfolios, the most significant predictor of higher perceived vulnerability was environmental worldview, specifically a belief that climate change is occurring. Motivation to adapt is also influenced by the sentiment that the impacts of climate change are more urgent and consequential than other problems; thus, we also evaluate how concern levels for environmental issues compare to other challenges that may affect fishing success and wellbeing. While just under half think that they will be personally harmed by climate change, generally the fishers were more concerned about issues like costs and regulations than they were about environmental impacts. This assessment of perceptions highlights the importance of communication and addressing cognitive barriers to adaptation in the effort to develop climate resilient fisheries and fishing communities in the United States.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".