Racism, the environment, and persecution : environmental refugees in Tuvalu
Bibliographic record
Abstract
This paper explores the concept of environmental refugees through a literature review and discourse analysis of media coverage on Tuvalu. Tuvalu is predicted to be the first nation lost to sea level rise and its government has been active in attempting to secure a place of asylum for its citizens. Although the term 'environmental refugee' is not an official one, it is widely used. Therefore, a case study is used to illustrate how environmental refugees are constructed in the public eye. Using political economy and political ecology approach, the power dynamics that lead to disproportionate environmental destruction in poor, racialized areas as well as unequal access to migration are questioned. Looking at two major newspapers each from Canada, the US, Australia, and New Zealand, and one from Tuvalu, the discourse surrounding environmental refugees reveals how the term is constructed and used for varying agendas, from environmentalism to racial exclusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".