Bibliographic record
Abstract
Abstract Environmental change is growingly reported as an important driver of human migration. Among all environmental variables, water crises are the most critical factors. To date, patterns of interconnections between changes in water and migration are not yet clearly understood. Here, we explore these patterns through a systematic review that combined a quantitative text‐mining approach with qualitative thematic analysis. Our results generally concur with those of previous studies, which found that water‐driven migration usually occurs internally and that the population in low‐ and middle‐income countries and in dry regions are the most vulnerable and more likely to migrate or be displaced in the face of water‐related events. However, our causal network analysis highlights that water is not the only reason for migration: Its related problems could be major triggers driving people‐at‐risk to leave their original place. Based on observed evidence, water‐driven migration can be generally divided into four patterns: variability in water quantity, damaging water hazards and extremes, physical disturbances to water systems, and water pollution. These patterns are not independent but interconnected through multifaceted factors affecting people's livelihoods and their decisions to migrate. Understanding water‐migration dynamics requires systematic thinking of the interconnections between changes in water and in migration patterns, the investigation of interactions between fast and slow water variables and their dynamic link to other socioeconomic variables, an integrated water‐migration database to help identify early‐warning signals of damaging water hazards that may result in undesirable migration, and targeted water policies that focus on building the resilience of vulnerable regions and population to climate change. This article is categorized under: Human Water > Value of Water
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".