Introduction Environmentally Induced Displacement and Forced Migration
Bibliographic record
Abstract
Disappearing coastlines, fields and homes flooded by rising waters, lands left cracked and barren by desertification, a snowpack shrinking in circumpolar regions year by year—these are only a few of the iconic images of climate change that have evoked discussion, debate, and consternation within communities both global and local. Equally alarming has been the threat of what such degraded and destroyed landscapes might mean for those who depend upon them for their livelihoods—as their homes, as their means of sustenance, and as an integral part of their cultural and social lives. A mass of humanity on the move—some suggest 50 million, 150 million, perhaps even a billion people1—the spectre of those forced to flee not as the result of war or conflict but rather a changed environment haunts the imaginaries of national governments, international institutions, and public discourse alike. Are these environmental refugees? Should they be granted the same protections and support as those who can prove their fear of and flight from persecution? Do the sheer numbers contemplated by the scale of the events and factors threaten to overwhelm the international refugee system?
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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 | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.001 |
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".