The indigenous Pacific Islands’ resilience against climate injustice
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".