Exploring Women’s Migration through Bibliometrics: Trends and Research Networks
Bibliographic record
Abstract
In recent decades, there has been an increase in scientific interest in women’s migration, reflecting the globalisation of migration flows and increased gender sensitivity in research. The aim is to explore the mapping of the scientific field devoted to women’s migration through analysis to identify key trends, thematic areas, and international scientific collaborations. The Scopus database covering the period from 1979 to 2025 is used as an empirical base. The sample includes 860 articles selected based on relevant keywords related to women’s migration. Drawing on a dataset of 860 peer-reviewed articles from the Scopus database spanning 1979–2025, the analysis employs advanced bibliometric tools including VOSviewer and Bibliometrix (R package). The study examines publication dynamics, prolific authors and journals, influential countries, citation patterns, and co-occurrence networks of keywords. The results reveal six dominant thematic areas: labour migration, gender discrimination, marital migration, cultural norms, socio-economic mobility, and structural barriers. The findings reveal six dominant thematic clusters: labour migration, gender discrimination, marital migration, cultural norms, socio-economic mobility, and structural barriers. The United States (298 articles), the United Kingdom (170), and Canada (79) emerged as the most productive contributors. While research is primarily concentrated in North America and Europe, academic interest is steadily increasing in Southeast Asia, East Asia, and Latin America. This article will guide future research by providing a scientific map of studies that are at the intersection of migration and gender issues.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.103 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".