MAPPING GLOBAL RESEARCH ON GENDER STEREOTYPES: A BIBLIOMETRIC ANALYSIS THROUGH 2024 BASED ON THE SCOPUS DATABASE

Abstract

This study aims to map and analyze the intellectual structure and emerging trends in global research on gender stereotypes from 2000 to 2024 through a comprehensive bibliometric approach. The objective is to identify key publication patterns, influential contributors, thematic developments, and geographic distributions while assessing the evolution and interdisciplinary nature of the field. A refined search was conducted on the Scopus database using the query TITLE (gender AND stereotypes) with subject area and document type filters, yielding a final dataset of 168 publications. The data were analyzed using VOSviewer for co-authorship, keyword co-occurrence, and citation network visualizations, and Microsoft Excel for publication trends and frequency counts. The analysis proceeded through three phases: data collection and cleaning, network mapping, and interpretive analysis. Findings reveal a significant increase in scholarly output after 2020, with research clusters forming around: (1) Gender Roles and Socialization, (2) Implicit Biases and Stereotype Threat, and (3) Algorithmic and Media-Driven Biases. Psychology remains the dominant discipline, but contributions from AI ethics, cognitive neuroscience, and behavioral economics are emerging, albeit with limited cross-disciplinary integration. The United States, Germany, and Spain lead in publication output, while Vietnam, India, and Bangladesh represent growing contributors from the Global South. This study provides a foundational overview of the intellectual landscape of gender stereotype research. The findings underscore the need for more interdisciplinary collaboration and methodological diversification, particularly in connecting cognitive science, artificial intelligence, and policy research. These insights have implications for designing evidence-based strategies to address gender bias in education, employment, and digital platforms.