Limit(ation)s, sustainability, and the future of climate migration
Bibliographic record
Abstract
Climate change and human migration are two of the world's most pressing issues, as many populations rely on migration as an adaptation strategy to climatic stressors. Human experiences of, and responses to, climate stress are uneven and mediated by resource privilege. In many communities in the Global South, climate vulnerabilities are exacerbated by fragile ecological conditions due to geographical positioning, and many already marginalised groups shoulder a disproportionate burden of climate change effects, despite contributing the least to this problem. In parts of sub-Saharan Africa, rapidly deteriorating climatic conditions imply that climate vulnerabilities may be reproduced in migration destination areas as well. Drawing on primary research conducted in Ghana, we illustrate how migration may present limitations and thus serve as an unsustainable adaptation strategy towards climate change for agrarian and structurally marginalised groups. We highlight the need for more discussions of sustainability in issues of climate migration in Ghana and similar contexts of the Global South, and the urgency of mitigating climate change globally. We conclude with calls for more nuanced understandings of the futures of climate migration as an adaptive strategy.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".