INVESTIGATING GENDER BIAS AND ROLE ALLOCATION PATTERNS IN ARCHIVAL ORGANIZATIONS THROUGH HR ANALYTICS

Abstract

Archival organizations are important for maintaining cultural heritage and institutional memory, yet gender bias and unequal role allocation continue to influence these organizational structures. This study examines the patterns of gender-based inequities in job assignments, promotions, and leadership roles in archival organizations using Human Resource (HR) analytics. This study analyzes longitudinal HR data from several archival organizations to determine where implicit bias and structural inequities in how roles are allocated. In this study, data amalgamated from individual, longitudinal HR datasets are organized into statistical models and machine learning practices to understand the interactions of gender with job type, timelines, and performance ratings to create organizational mobility. Findings point to gendered clustering in administrative versus decision making roles along vertical and horizontal segregation. This paper provides predictive models to map decision points within HR pipeline where bias is likely to occur, describes data driven potential methods to promote equitable talent development, and to promote inclusive policy changes. The implications of this study can assist to inform bias aware, diversity based HR practices within the archival sector.