The Algorithmic Reputation Gap: How Platform Mediated Trust Redistributes Social Capital Among Gig Economy Workers
DOI:
https://doi.org/10.21590/Keywords:
Algorithmic reputation, social capital, gig economy, platform labor, digital inequalityAbstract
The rapid expansion of platform mediated labor has introduced novel mechanisms for evaluating trust and competence, yet the social consequences of these systems remain undertheorized. This paper investigates how algorithmic reputation scores affect the distribution of social capital among gig economy workers, specifically focusing on ride hail and food delivery platforms in three distinct urban contexts. Drawing on a mixed methods research design that combines longitudinal quantitative analysis of 1,240 worker profiles with 62 semistructured interviews, the study examines whether platform generated ratings function as neutral proxies for service quality or as instruments that systematically disadvantage already precarious workers. Findings indicate a significant algorithmic reputation gap: workers from lower socioeconomic backgrounds, those with nonstandard name markers, and individuals without access to informal rating management networks accumulate negative rating trajectories at disproportionately higher rates. These disparities persist even when controlling for service performance metrics, suggesting that reputation systems embed preexisting social hierarchies rather than merely reflecting transactional outcomes. The paper advances a theoretical framework of algorithmic reputational capital to explain how platform governance produces new forms of stratification. We argue that digital reputation operates as a convertible asset, yet its accumulation follows path dependent patterns that reinforce rather than disrupt structural inequality. These findings carry implications for platform regulation, labor rights advocacy, and the sociology of digital stratification.
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