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Record W2912550921 · doi:10.25904/1912/2623

Future climate narratives: knowledge informing climate change adaptation

2018· dissertation· en· W2912550921 on OpenAlexfundaboutno aff
Liese Coulter

Bibliographic record

VenueGriffith Research Online (Griffith University, Queensland, Australia) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsnot available
FundersGriffith UniversitySimon Fraser UniversityCommonwealth Scientific and Industrial Research OrganisationPacific Institute for Climate Solutions
KeywordsClimate changeVulnerability (computing)Adaptation (eye)Thematic analysisNarrativeEnvironmental resource managementPolitical economy of climate changeAgency (philosophy)TypologyPolitical scienceGeographyPublic relationsEnvironmental planningSociologyPsychologyQualitative researchSocial scienceEcology

Abstract

fetched live from OpenAlex

In the current era, recognized by some as the Anthropocene, consequences from climate change are affecting ecosystems, societies and economies; making it vital to enact adaptation measures to manage impacts that cannot be avoided. Significant resources and attention have been invested to improve climate knowledge and its communication on a global level, which is essential for adaptation. However, some facility with prospection, or future thinking is also needed to plan for uncertain and future-oriented risks. Future thinking is a cognitive task initiated by individuals who may be engaged in planning for their own family or community. Such autonomous adaptation is not well studied but may have a profound effect on the future trajectory of both climate and society. Therefore, this research focused on what future climate narratives are being constructed and shared by those engaged in working with climate change knowledge; and to share those more broadly. In this research, interviews with Australian and Canadian professionals who worked with climate change in research, policy, and practice were analysed to gauge in what way their climate knowledge was linked to autonomous adaptation in personal circles. A novel Future Climate Narrative (FCN) typology was developed as a structural guide for qualitative analysis, informed by literature relating to future thinking, climate change adaptation and narrative communication. Consequently, inductive thematic analysis identified four main climate change adaptation narratives focused on: Distance, Vulnerability, Agency, and Change. The research finds that even well-informed professionals who are willing to address climate change in public, are reluctant to discuss the topic in personal and social circles; instead, engaging in Distance Narratives that position climate issues as affecting future generations and faraway lands. Participants made binary assessments when using Vulnerability and Agency Narratives to depict threats as either negligible, due to high social capacity to adapt and so requiring no additional personal agency; or as overwhelming, if that social net was insufficient and therefore, personal agency would be insignificant in the face of global change. Neither assessment motivated improving personal agency to adapt to climate change. In contrast, the few participants who engage in Change Narratives express a sense of personal agency to enable transformation as a response to expected disruptions and display a facility to mentally link with the past to inform the future. However, the incompatibility of the Change and Distance Narratives creates a barrier to sharing plans for autonomous adaptation in social circles. To develop well informed and well-shared climate change adaptation narratives, old understandings need to be updated with increased focus on future thinking to continually imagine and re-imagine adaptive behaviours. Otherwise, possible benefits from current adaptive advantages may not be realized. Considering autonomous adaptation to climate change, the convergent contexts of climate change, the imagined future and shared personal narratives chart a small but growing field of academic inquiry, to which this research contributes.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0050.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.645
GPT teacher head0.534
Teacher spread0.111 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2018
Admission routes2
Has abstractyes

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