PREDICTOR VARIABLES OF IMMIGRANT ROOTING: A MODEL USING MACHINE LEARNING

Jorge Díaz-Ramírez; José Berríos-Riquelme; Jorge Maluenda-Albornoz; Olaya Grau-Rengifo; Gustavo Castillo-Rozas; Carla Vidal-Figueroa

PREDICTOR VARIABLES OF IMMIGRANT ROOTING: A MODEL USING MACHINE LEARNING

Jorge Díaz-Ramírez
, José Berríos-Riquelme
, Jorge Maluenda-Albornoz
, Olaya Grau-Rengifo
, Gustavo Castillo-Rozas
, Carla Vidal-Figueroa

University of Valencia
Universidad de Concepción
Complutense University

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Rooting is essential for the social and cultural integration of immigrants in the host country. This research aims to use machine learning techniques to explore predictor variables of the rooting process of South American immigrants in Spain. The study follows a cross-sectional design, with a sample of 634 immigrants. The main results indicate that low perceived prejudice, contact with Spaniards, and stable employment are important predictors of rooting. Future studies need to explore more deeply into the quality of contact and the work experience of immigrants. The discussion is focuses on the relationship between theory and results, with suggestions for improving the prediction of the rooting with machine learning. The results of this study are valuable for public policies aimed at immigrant integration.

Translated title of the contribution	VARIABLES PREDICTORAS DEL ARRAIGO INMIGRANTE: UN MODELO USANDO APRENDIZAJE AUTOMÁTICO
Original language	English
Pages (from-to)	701-710
Number of pages	10
Journal	Interciencia
Volume	49
Issue number	12
State	Published - Dec 2024

Keywords

/ Intergroup contact
Machine learning
Rooting
Social integration
South American Immigrants /

Cite this

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title = "PREDICTOR VARIABLES OF IMMIGRANT ROOTING: A MODEL USING MACHINE LEARNING",

abstract = "Rooting is essential for the social and cultural integration of immigrants in the host country. This research aims to use machine learning techniques to explore predictor variables of the rooting process of South American immigrants in Spain. The study follows a cross-sectional design, with a sample of 634 immigrants. The main results indicate that low perceived prejudice, contact with Spaniards, and stable employment are important predictors of rooting. Future studies need to explore more deeply into the quality of contact and the work experience of immigrants. The discussion is focuses on the relationship between theory and results, with suggestions for improving the prediction of the rooting with machine learning. The results of this study are valuable for public policies aimed at immigrant integration.",

keywords = "/ Intergroup contact, Machine learning, Rooting, Social integration, South American Immigrants /",

author = "Jorge D{\'i}az-Ram{\'i}rez and Jos{\'e} Berr{\'i}os-Riquelme and Jorge Maluenda-Albornoz and Olaya Grau-Rengifo and Gustavo Castillo-Rozas and Carla Vidal-Figueroa",

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T2 - A MODEL USING MACHINE LEARNING

AU - Díaz-Ramírez, Jorge

AU - Berríos-Riquelme, José

AU - Maluenda-Albornoz, Jorge

AU - Grau-Rengifo, Olaya

AU - Castillo-Rozas, Gustavo

AU - Vidal-Figueroa, Carla

PY - 2024/12

Y1 - 2024/12

N2 - Rooting is essential for the social and cultural integration of immigrants in the host country. This research aims to use machine learning techniques to explore predictor variables of the rooting process of South American immigrants in Spain. The study follows a cross-sectional design, with a sample of 634 immigrants. The main results indicate that low perceived prejudice, contact with Spaniards, and stable employment are important predictors of rooting. Future studies need to explore more deeply into the quality of contact and the work experience of immigrants. The discussion is focuses on the relationship between theory and results, with suggestions for improving the prediction of the rooting with machine learning. The results of this study are valuable for public policies aimed at immigrant integration.

AB - Rooting is essential for the social and cultural integration of immigrants in the host country. This research aims to use machine learning techniques to explore predictor variables of the rooting process of South American immigrants in Spain. The study follows a cross-sectional design, with a sample of 634 immigrants. The main results indicate that low perceived prejudice, contact with Spaniards, and stable employment are important predictors of rooting. Future studies need to explore more deeply into the quality of contact and the work experience of immigrants. The discussion is focuses on the relationship between theory and results, with suggestions for improving the prediction of the rooting with machine learning. The results of this study are valuable for public policies aimed at immigrant integration.

KW - / Intergroup contact

KW - Machine learning

KW - Rooting

KW - Social integration

KW - South American Immigrants /

UR - https://www.scopus.com/pages/publications/85213857417

M3 - Article

AN - SCOPUS:85213857417

SN - 0378-1844

VL - 49

SP - 701

EP - 710

JO - Interciencia

JF - Interciencia

IS - 12

ER -

PREDICTOR VARIABLES OF IMMIGRANT ROOTING: A MODEL USING MACHINE LEARNING

Abstract

Keywords

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