Bibliographic record
Abstract
Introduction Environmental migration has received increased scholarly and policy attention in the last decade. Though environmental drivers have always played a role in migration movements, the number of natural disasters has seemed to be on the rise, and their severity appears to be worsening, possibly due to the first impacts of climate change. Research on environment-related migration flows in recent times has shed new light on the linkages between environmental changes and migration. History shows numerous examples of migrations associated with environmental changes and disasters. In 1755, an earthquake destroyed most of Lisbon, inducing mass population displacements towards other parts of Portugal, with some of those displaced returning to the city later (Dynes 1997). The Dust Bowl migration in the US is another classic example of mass migration associated with environmental disaster. In that case, severe drought and soil-depleting agricultural techniques resulted in dust storms that pushed populations westward. Thousands of farmers from Oklahoma, Texas and Arkansas had no choice other than to sell their farms and move in the 1930s. The environmental ‘push’ factors are obvious in this migration decision, but it should be stressed that other socio-economic factors were at work as well. The migration took place within the context of the Great Depression (Hansen and Libecap 2004). Furthermore, the prospect of a better life in California played a crucial role as a ‘pull’ factor (Gregory 1991). More recently, massive population displacements were triggered by catastrophes such as the Sumatran tsunami in 2004, hurricane Katrina in 2005 and cyclone Nargis, which ravaged Burma in 2008. These disasters raised public awareness about the fate of a new kind of ‘refugee’. In addition to those displaced by sudden events, many more are also displacements by slow-onset events. Most of these are related to climate change: villages resettled in the South Pacific islands to escape the rise in sea-level, farmers and pastoralists moving to cities because desertification threatens their livelihoods in sub-Saharan Africa and Northern China, and Inuit communities displaced by the melting of the permafrost in Alaska. These population movements are not alike, but all can be presented as ‘environmental migration’ and envisioned through this lens.
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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 | 0.000 | 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) | 0.091 | 0.002 |
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; both teacher heads agree on what is shown here.
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".