Research Trends on Immigrant Teachers: A Bibliometric Study
Bibliographic record
Abstract
This study aims to explore the bibliometric characteristics of research on teacher immigration without a specific time frame. Indexed documents were retrieved from the Scopus database, and then analyzed using the VOSviewer software. The search yielded 438 articles representing 55 countries and 160 journals. Since 2017, the number of articles about this topic has increased. The United States, Canada and China were the three countries with the highest production in this field. Most articles were published in education and cultural studies journals. The institutions with the most active participation in publications from this field were from South-Africa and Canada. The most prolific author is Sadhana Manik; however, no avid producers of research were observed, and citation and collaboration among researchers is scarce. References to Latin-American countries were not found despite the increase in their migrant population in recent years. Therefore, the results call researchers to conduct local and collaborative research in order to deepen the knowledge about this minority, and develop public policies suitable for increasingly diverse populations. Addressing these research gaps and fostering international collaboration is essential to achieving a more complete understanding of teacher migration and its implications for education systems around the world. Received: 3 November 2024 / Accepted: 14 April 2025 / Published: 08 May 2025
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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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.110 | 0.168 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".