Exploring diverse perspectives of coastal resilience: The state of resilience model
Bibliographic record
Abstract
In the context of climate change, the term resilience was popularized by the field of ecology to describe how ecological systems respond to stress and has since been adopted and significantly adapted by various fields, including psychology, policy, urban planning, and engineering. The exact meaning of resilience has blurred over time. In the context of coastal hazards, “resilience” is a holistic idea that relates long and short-term physical hazards with societal and biological impacts and mitigation measures. However, applying this idea to community-based mitigation planning remains challenging due to: (1) the diverse meanings, perspectives, and applications of the term, (2) the tendency of the term to defer to the status quo, thereby neglecting the voices of historically marginalized populations, and (3) the non-participatory and quantitative nature of resilience studies, often depending on cost-benefit analyses. In this paper, an interdisciplinary team of researchers and practitioners develops and proposes a new conceptual model for coastal resilience that offers to help address these aforementioned challenges by focusing on meaningful community engagement. The goal of this paper is to introduce the pitfalls of existing interpretations of coastal resilience, describe the team-based approach applied to develop this framework, and provide a theoretical path forward that addresses the current challenges in describing coastal resilience. This new framework (a) integrates relevant factors of coastal resilience including hazards, exposure, vulnerability, adaptation, mitigation and preparedness to qualitatively explore a community’s perception and state of resilience which (b) transcends existing models and (c) can be interpreted through a variety of perspectives. This model can be applied to document and assess locally differential understandings of coastal resilience and to engage communities in reflections of their individual and collective sense of resilience.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".