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Record W4243147683 · doi:10.31230/osf.io/2h8b4

Communities and change in the anthropocene: understanding social-ecological vulnerability and planning adaptations to multiple interacting exposures

2018· preprint· en· W4243147683 on OpenAlexafffund
Nathan Bennett, Jessica Blythe, Stephen Tyler, Natalie C. Ban

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaLiber Ero FoundationUniversity of Victoria
KeywordsVulnerability (computing)TypologyAdaptation (eye)Vulnerability assessmentEnvironmental resource managementContext (archaeology)Conceptual frameworkEnvironmental planningClimate changeScholarshipGeographySociologyEcologyPolitical sciencePsychological resilienceComputer scienceSocial sciencePsychologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

The majority of vulnerability and adaptation scholarship, policies and programs focus exclusively on climate change or global environmental change. Yet, individuals, communities and sectors experience a broad array of multi-scalar and multi-temporal, social, political, economic and environmental changes to which they are vulnerable and must adapt. While extensive theoretical—and increasingly empirical—work suggests the need to explore multiple exposures, a clear conceptual framework which would facilitate analysis of vulnerability and adaptation to multiple interacting socioeconomic and biophysical changes is lacking. This review and synthesis paper aims to fill this gap through presenting a conceptual framework for integrating multiple exposures into vulnerability analysis and adaptation planning. To support applications of the framework and facilitate assessments and comparative analyses of community vulnerability, we develop a comprehensive typology of drivers and exposures experienced by coastal communities. Our results reveal essential elements of a pragmatic approach for local-scale vulnerability analysis and for planning appropriate adaptations within the context of multiple interacting exposures. We also identify methodologies for characterizing exposures and impacts, exploring interactions and identifying and prioritizing responses. This review focuses on coastal communities; however, we believe the framework, typology and approach will be useful for understanding vulnerability and planning adaptation to multiple exposures in various social-ecological contexts.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.573
GPT teacher head0.452
Teacher spread0.121 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes2
Has abstractyes

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