The legacy of migration in response to climate stress: learning from the<scp>G</scp>ilbertese resettlement in the<scp>S</scp>olomon<scp>I</scp>slands
Bibliographic record
Abstract
Abstract The long‐term threat of sea‐level rise to coral atoll and reef island communities inKiribati,Tuvalu and other nations has raised the possibility of international migration. Historical resettlements in thePacific may provide valuable insight into the long‐term effect of future climate change‐related migration on communities. This study evaluates the challenges faced byGilbertese people resettled from modern‐dayKiribati to Ghizo in theSolomonIslands by theBritish colonial administration in the mid‐1900s. Drawing upon field interviews (n=45) conducted in 2011 and the available historical literature, the study examines the circumstances of the initial failed resettlement in the equatorial PhoenixIslands, the subsequent relocation toGhizo, and the recent concerns of theGilbertese inGhizo. Focus is placed on the struggle to recover from the 2007 tsunami that devastated the unprepared community. The analysis reveals that uncertainty about land tenure (raised by 61% of respondents) persists 60 years after resettlement, and is linked to the ability to recover from the tsunami, tensions with the Melanesian population, concerns over political representation, cultural decline, and education and employment opportunities. The Gilbertese experience can serve as a cautionary tale for policymakers considering mechanisms for facilitating climate change‐related migration.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".