forthcoming. From climate refugees to climate conflict: Who is taking the heat for global warming? In: Salih, M. (ed), Climate Change and Sustainable Development: New Challenges for Poverty Reduction, Edward Elgar Publishers. _____2006. Liberal Ends, Illi
Bibliographic record
Abstract
The spring and summer of 2007 brought a spate of alarming articles and reports about the security implications of climate change. Writing in the April issue of the Atlantic Monthly, journalist Stephan Faris attributed the violence in Darfur in large part to global warming-induced environmental degradation and drought. Several months later a report on the Sudan by the United Nations Environment Program (UNEP) drew similar conclusions, arguing that a combination of demographic pressures, resource scarcity and climate change were at the root of ethnic conflict in the region and increasingly threatened security in other parts of Africa as well (UNEP 2007). Along with the Darfur stories came other dire predictions about the threat of so-called “climate refugees. ” In May, the U.K.-based NGO Christian Aid (2007a) released a report entitled Human Tide: The Real Migration Crisis that painted an apocalyptic scenario of millions of displaced climate refugees roaming the globe and wreaking havoc, creating “a world of many more Darfurs ” (Christian Aid 2007b). Journalists and pundits alike jumped on the bandwagon. Writing in Scientific American online, Columbia University economist Jeffrey D. Sachs warned that climate change could soon force “hundreds of millions ” of people to relocate (Sachs 2007). In the New York Times Canadian political scientist Thomas Homer-Dixon claimed that “Climate stress may
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.063 | 0.018 |
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".