Climate humanitarian visa: international migration opportunities as post-disaster humanitarian intervention
Bibliographic record
Abstract
Abstract With global action being outpaced by climate change impacts, communities in climate-vulnerable countries are at increased risk of incurring climate-induced losses and damages. In the last few years, disasters from extreme weather events such as typhoons have increased and have breached records, with typhoon Haiyan being the strongest ever typhoon to make landfall. Such an event solicited global compassion and altruism where Canada and the USA, apart from doling out traditional humanitarian aid, also offered immigration relief opportunities to typhoon Haiyan victims who have familial connections to their residents. Drawing from these immigration relief interventions, this paper uses a sociopolitical approach in proposing a climate humanitarian visa that would be offered to climate change victims on the basis of transnational family networks and skilled labor. Noting that several countries such as in Europe have demographic deficits and labor shortages, such a scheme would benefit both climate change victims and receiving countries. To counter the risk of selective compassion against economically trapped populations, potential receiving countries could provide skills upgrading geared toward addressing their labor shortages through their existing development programs. While migration is only one strategy in a spectrum of responses to climate change impacts, a climate humanitarian visa could provide climate change victims a legal choice for mobility while invoking altruism, hospitality, and compassion from potential receiving countries, whether or not they historically cause climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".