Bibliographic record
Abstract
Purpose “No climate change, no climate refugees”. On the basis of this theme, this paper aims to propose a method for undertaking the responsibility for climate refugees literally uprooted by liable climate polluting countries. It also considers the historical past, culture, geopolitics, imposed wars, economic oppression and fragile governance to understand the holistic scenario of vulnerability to climate change. Design/methodology/approach This paper is organized around three distinct aspects of dealing with extreme climatic events – vulnerability as part of making the preparedness and response process fragile (past), climate change as a hazard driver (present) and rehabilitating the climate refugees (future). Bangladesh is used as an example that represents a top victim country to climatic extreme events from many countries with similar baseline characteristics. The top 20 countries accounting for approximately 82 per cent of the total global carbon dioxide (CO 2 ) emissions are considered for model development by analysing the parameters – per capita CO 2 emissions, ecological footprint, gross national income and human development index. Findings Results suggest that under present circumstances, Australia and the USA each should take responsibility of 10 per cent each of the overall global share of climate refugees, followed by Canada and Saudi Arabia (9 per cent each), South Korea (7 per cent) and Russia, Germany and Japan (6 per cent each). As there is no international convention for protecting climate refugees yet, the victims either end up in detention camps or are refused shelter in safer places or countries. There is a dire need to address the climate refugee crisis as these people face greater political risks. Originality/value This paper provides a critical overview of accommodating the climate refugees (those who have no means for bouncing back) by the liable countries. It proposes an innovative method by considering the status of climate pollution, resource consumption, economy and human development rankings to address the problem by bringing humanitarian justice to the ultimate climate refugees.
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 | 0.005 | 0.008 |
| 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 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".