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Record W4401973956 · doi:10.1080/14755610.2024.2373104

The indigenous Pacific Islands’ resilience against climate injustice

2024· article· en· W4401973956 on OpenAlexaff
Baiq Wardhani, Vinsensio Dugis, Moch Yunus, Dianming Wu

Bibliographic record

VenueCulture and Religion · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsCarleton University
Fundersnot available
KeywordsIndigenousResilience (materials science)InjusticeClimate justiceGeographyPolitical scienceClimate changeOceanographyGeologyEcology

Abstract

fetched live from OpenAlex

Small island developing states (SIDS), particularly the low-lying islands situated in Oceania within the South Pacific region, are subject to climate injustice; wherein the issue of climate change is intricately linked to their very survival. While mitigation strategies are enacted through the facilitation of knowledge generation and international collaboration, adaptive measures are implemented via a religious and spiritual framework. This distinctive approach represents a defining feature of the indigenous inhabitants within the Pacific SIDS community, serving as a socio-cultural determinant that influences specific outlooks held by Pacific denizens concerning climate variability. This paper aims to explore the rationale behind the inclination of traditional Pacific societies towards embracing a spiritual stance in addressing climate adaptation. It is contended that in confronting climate injustices, the indigenous populations of the Pacific region tend to draw more heavily upon traditional practices and the incorporation of local knowledge alongside conventional approaches, such as scientific, technological, and diplomatic measures. The phenomenon of climate change in the Pacific SIDS is emblematic of susceptibility, calamity, and discourses revolving around trepidation and ambiguity (aporia), necessitating strategies that extend beyond those rooted in secular-Western ideologies.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.030
GPT teacher head0.302
Teacher spread0.272 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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