International migration opportunities as post-disaster humanitarian intervention
Bibliographic record
Abstract
The frequency and intensity of natural disasters from climate events have been increasing in\nthe last few years. While scientists are careful in causally linking these natural disasters to\nclimate change, the record-breaking extreme climate events such as typhoon Haiyan in the\nPhilippines speak for itself. Formerly a laggard in climate talks, adaptation has now gained\nfooting not just in the UNFCCC but also among different countries. Decision 1/CP.16 also\nknown as The Cancun Agreements invites all parties to the UNFCCC to enhance action on\nadaptation and undertake measures with regard to climate change induced displacement\nand migration. It is exactly this COP decision, which motivated the governments of Norway\nand Switzerland to establish the Nansen Initiative and craft a protection agenda for people\nwho are at risk of disaster-induced cross-border displacement. Despite these efforts, there\nhas yet to be a legally binding migration treaty that climate change victims can invoke. In this\npaper, we will look into the feasibility of immigration opportunities as humanitarian aid for\nvictims of extreme climate events. Inspired by US and Canada immigration relief measures\nfor typhoon Haiyan victims in the Philippines, we use a socio-political approach in constructing\nan immigration humanitarian model, which we would like to recommend as a potential\nhumanitarian intervention after climate disasters. This recommendation is not only intended\nto address UNFCCC’s Decision 2/CP.19 (the Warsaw International Mechanism on Loss and\nDamage) but to also provoke ambition and compassion from countries that are historically\nresponsible for climate change.
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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 | 0.003 | 0.004 |
| 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 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".