Climate change and human mobility : global challenges to the social sciences
Bibliographic record
Abstract
1. Introduction: climate change and human mobility Kirsten Hastrup and Karen Fog Olwig 2. Leaving home: how can historic human movement inform the future? Carole Crumley 3. Inuit and climate change in the prehistoric eastern Arctic: a perspective from Greenland Mikkel Sorensen 4. Dehumanising the uprooted: lessons from Iceland in the Little Ice Age Kirsten Hastrup 5. Relocation of Reef and Atoll Island communities as an adaptation to climate change? Learning from experience in the Solomon Islands Thomas Birk 6. Contextualising links between migration and environmental change in northern Ethiopia James Morrissey 7. On the risks of engineering mobility to reduce vulnerability to climate change: insights from a small island state Jon Barnett 8. Mobility, climate change and social dynamics in the Arctic: the creation of new horizons of expectation and the role of the community Frank Sejersen 9. Land grab in Africa: resilience for whom? Quentin Gausset and Michael Whyte 10. Climate change, migration and Christianity in Oceania Wolfgang Kempf 11. Climate-induced migration and conflict: what are the links? Christian Webersik.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".