On the risks of engineering mobility to reduce vulnerability to climate change: insights from a small island state
Bibliographic record
Abstract
This chapter explains the likely consequences of proposals to resettle large numbers of people away from the Pacific Islands for the people left behind. It does this by describing the effects of large-scale migration away from the small island state of Niue, which is a very good analogue from which lessons for other islands can be drawn. The chapter begins by examining the discourse on large-scale migration as a solution to save the people of the Pacific Islands from the impacts of climate change. The discourse of draining the people from these remote island backwaters of the world persists even though understanding of vulnerability and adaptation to climate change in the Pacific Islands remains extremely limited. In this discourse there is little concern for the needs and rights of migrants, and no consideration of the consequences of such movements for those people who cannot or do not wish to move. It is this latter issue that this chapter examines. There has been large-scale migration from Niue since 1971, to the extent that 80 per cent of the people born in Niue now live in New Zealand. There are six principal effects of this depopulation on those who remain on the island, namely that it leads to: distortions in markets; obsolescent political and administrative institutions; a hyper-concentration of social capital; increased demands on labour; difficulties in defining and maintaining that which is ‘traditional’; and an erosion of Niuean identity. Based on this examination, the chapter argues that migration is likely to be an impact of climate change as much as it is to be an adaptation. Mitigation and adaptation must therefore be the preferred strategies, although there may be scope for carefully managed labour migration as part of a suite of adaptation strategies.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".