Variations and Parallels in Climate Change-Induced Migration Models: Customary Land Tenure in Francophone Pacific Islands
Bibliographic record
Abstract
It is tempting to assume that across all Pacific Islands, potential climate change-induced migration (PCCIM) due to sea-level rise can be approached in a unified manner. However, the diversity of the Pacific Islands requires an in-depth analysis in order to establish culturally coherent migration models. The possibilities and limits that customary land tenure can offer in this context on islands of the three Pacific French overseas territories Wallis & Futuna (Wallis, Futuna), French Polynesia (Rangiroa) and New Caledonia (Lifou) are analysed through four lenses: the intergenerational transfer of land rights, the distribution of land plots (geographically and between families), the extent of power exercised by customary authorities, and the different types of ownership or usufruct. The examination of common threads and variations shows that guiding principles (access to land in the interior of a respective island, strength of land rights on a certain plot, infrastructure issues, concepts of mobility, importance of primary land ownership, importance of primogeniture, and potential inter-island access) are shared to different degrees across the islands. The fourfold matrix allows a robust analysis of the possibilities in the context of PCCIM in different locations through examining parallels, differences, advantages, and disadvantages of the different systems.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".