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Record W2889897084 · doi:10.3197/ge.2018.110209

Environmental History and the Concept of Agency: Improving Understanding of Local Conditions and Adaptations to Climate Change in Seven Coastal Communities

2018· article· en· W2889897084 on OpenAlexaff
Gregory Kennedy, Mélanie Raimonet, Matthew Berman, Ndickou Gaye, Jean-Michel Huctin, Thomson Kaleekal, Jean‐Paul Vanderlinden

Bibliographic record

VenueGlobal Environment · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsAcadia University
FundersAgence Nationale de la Recherche
KeywordsAgency (philosophy)Adaptation (eye)Construct (python library)DisciplineEnvironmental changeClimate changeProcess (computing)ColonialismSociologyOrder (exchange)Environmental ethicsEnvironmental resource managementPolitical scienceEcologySocial scienceComputer sciencePsychologyBusiness

Abstract

fetched live from OpenAlex

Abstract This article provides a synthesis of the results from seven global research sites working together to study adaptation to climate change in coastal communities under the moniker ARTISTICC (www.artisticc.net). It first aims to share these research results in order to demonstrate two general themes that emerge from our analysis and can help improve our understanding of community responses to environmental change broadly speaking. These themes are the continuity of environmental change and the legacy of colonialism. The goal is to demonstrate that comparisons across research sites are possible if an appropriate transdisciplinary framework is in place and also that environmental history is required to understand the past if we are to effectively tackle present conditions. Secondly, this paper offers reflections on the concepts of agency and adaptation and how the methodological divide between historians and social scientists can be further bridged to great benefit for all concerned. By being more reflective on our own disciplinary cultures, we can co-construct knowledge about how community cultures operate. The agency of local actors is central to understanding past choices and present obstacles to successful adaptation. Indeed, we must better appreciate the goals of local actors if we are to know what success looks like to them. Adaptation, whether adjustment or transformation, is often a long-term, complex process.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.216
Teacher spread0.191 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2018
Admission routes1
Has abstractyes

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