Research Trends on Immigrant Teachers: A Bibliometric Study
Why this work is in the frame
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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
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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.035 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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