The Globalization of International Migration? A Conceptual and Data‐Driven Synthesis
Bibliographic record
Abstract
Although the globalization of international migration is commonly accepted as a general tendency in contemporary migration patterns (de Haas, Castles, and Miller 2020, 9), the corresponding body of empirical evidence is mixed and fragmented. Our review of global migration patterns over the past half-century highlights how the theories, expectations, and ultimately findings may vary depending on the specific definitions, vantage points, and measures being used. In this paper, we provide a simpler and integrated account of the globalization of international migration that includes a corresponding empirical template to quantify the relative importance of two processes at work: the intensity and connectivity of international migration. Using recent estimates of country-to-country migration flows every five years from 1990-1995 to 2015-2020, our analysis using demographic decomposition and group-based multitrajectory modeling highlights the dynamic relationship between intensity and connectivity from both the global and country vantage points. Our work in this paper provides a starting point in the form of a much-needed empirical template, one that is also highly flexible and customizable, for future research on the globalization of international migration to coalesce around and use going forward.
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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".