The eco‐island trap: climate change mitigation and conspicuous sustainability
Bibliographic record
Abstract
Small islands worldwide are increasingly turning to conspicuous sustainability as a development strategy. Island spatiality encourages renewable energy and sustainability initiatives that emphasise iconicity and are undertaken in order to gain competitive advantage, strengthen sustainable tourism or ecotourism, claim undue credit, distract from failures of governance or obviate the need for more comprehensive policy action. Without necessarily contributing significantly to climate change mitigation, the pursuit of eco‐island status can raise costs without raising income, distract from more pressing social and environmental problems, lead to competitive sustainability and provide green cover behind which communities can maintain unsustainable practices. We argue that eco‐islands do not successfully encourage wider sustainable development and climate change mitigation. Instead, island communities may place themselves in eco‐island traps. Islands may invest in inefficient or ineffective renewable energy and sustainability initiatives in order to maintain illusory eco‐island status for the benefit of ecotourism, thereby becoming trapped by the eco‐label. Islands may also chase the diminishing returns of ever‐more comprehensive and difficult to achieve sustainability, becoming trapped into serving as eco‐island exemplars. We conclude by arguing that island communities should pursue locally contextualised development, potentially focused on climate change adaptation, rather than focus on an eco‐island status that is oriented toward place branding and ecotourism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".