Evolution of climate-related migration and displacement in IPCC reporting
Bibliographic record
Abstract
Concerns about climate change impacts on migration and displacement have been expressed regularly by the Intergovernmental Panel on Climate Change (IPCC) since its first report in 1990. The 2022 Working Group II Sixth Assessment Report (AR6) presented the most comprehensive IPCC assessment to date of how climate affects migration and displacement, emphasizing complex causal linkages and multidirectionality of outcomes. Climate-migration linkages have not always been presented in such a nuanced way by the IPCC. Here we present results from a systematic identification and analysis of migration and displacement messaging in IPCC reports published prior to AR6. We chronicle an evolution from 1990s reports that stoked fears of environmental refugees and suggested "solutions" to prevent migration, to reports from the early 2000s that considered how to manage millions of displaced people and the potential financial costs, to more recent reports that considered migration and displacement within the wider context of human security. We identify inconsistencies in messaging about migration and displacement between reports (and sometimes within the same report) that persisted through Special Reports published in 2018 and 2019. We conclude by identifying topics and themes that by virtue of omission or under-reporting may warrant greater attention in future IPCC reports. This report also includes a visual timeline summarizing key developments. A link is provided at the end of this manuscript (p30) to MS Excel Tables that identify and summarize each occurrence of substantive discussion of migration, displacement and related topics in each IPCC report.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".