Agent-based modeling of environment-migration linkages: a review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental change can lead to human migration and vice versa. Agent-based models (ABMs) are valuable tools to study these linkages because they can represent individual migration decisions of human actors. Indeed, there is an increasing, yet small, number of ABMs that consider the natural environment in rural migration processes. Therefore, we reviewed 15 ABMs of environment-migration linkages in rural contexts to synthesize the current state of the art. The reviewed ABMs are mostly applied in tropical contexts, serve a wide range of purposes, and cover diverse scales and types of environmental factors, migration processes, and social-ecological feedbacks. We identified potential for future model development with respect to the (1) complexity of environmental influence factors, (2) representation of relevant migration flows, and (3) type of social-ecological couplings. We found that existing models tend to not include fully integrated feedbacks and provide recommendations for the further development of ABMs to contribute to an understanding of the environment-migration-nexus in the future.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it