Creating Sustainable Pacific Environments during the Anthropocene
Bibliographic record
Abstract
Pacific Island nations are some of the most vulnerable nations on Earth to the negative consequences of global warming. Low-lying atoll nations in the Pacific are already experiencing climate change in terms of the threat of sea level rise, ocean warming, and the intensification of tropical storms. Island nations are increasingly asserting their perspectives on global warming on the world stage as commitments made at international forums consistently fall short of the scientifically agreed minimum reduction of anthropogenic carbon emissions needed to avoid irreversible damage to planetary ecosystems. Less well documented, however, are moves domestically to climate-proof food security and enhance social resilience in island communities. After briefly outlining common threats faced by the majority of Pacific Island communities, this chapter focuses on avenues that are being explored in Federated States of Micronesia (Puas) and French Polynesia (Bambridge) to enhance climate resilience and create sustainable terrestrial and marine ecosystems. Both regions emphasize successful ancestral ways of resource management in partnership with Western science and technology. Colonial rule was more disruptive to these cultural institutions in French Polynesia than in Micronesia, but these institutions persisted in local practice in both locations to enable their recent revival. Both utilize culturally distinct ways of understanding the natural environments and humans’ place in them. Huge challenges remain. Results have been promising, however, and make a strong argument that humans need to respond quickly and flexibly to these latest environmental challenges, drawing inspiration from past generations of Pacific Islanders who colonized and enhanced some of the most challenging ecosystems on Earth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
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".