<p>How do institutional arrangements mediate the social effects of algorithmic power? Through comparative analysis of four configurations within China’s social credit system—market-mediated (Sesame Credit), state-administered (Rongcheng), professional (Hangzhou), and collaborative (Suzhou)—this study demonstrates that institutional logics function as generative mechanisms systematically shaping technology-society co-construction. We find that market, administrative, professional, and collaborative logics transform shared sociotechnical imaginaries into fundamentally different governance configurations across technical design, power distribution, norm construction, and subject formation. Critically, we theorize “differentiated algorithmic governmentalities” as a novel concept: while all systems cultivate self-regulating subjects through data-driven evaluation, they employ qualitatively distinct power mechanisms—seductive, disciplinary, expert, and networked governmentalities—operating through aspiration, compliance, professional identity, or coordination capacity. Our framework challenges both technological determinism and social constructivism, revealing that algorithmic effects emerge through institutionally specific mediations. This institutional diversity has profound implications for understanding digital governance and designing accountable algorithmic systems.</p>

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The Institutional Mediation of Algorithmic Power: China’s Social Credit System in Comparative Perspective

  • Yanfeng Gu,
  • Bingdao Zheng,
  • Ziying Li,
  • Yaning Li

摘要

How do institutional arrangements mediate the social effects of algorithmic power? Through comparative analysis of four configurations within China’s social credit system—market-mediated (Sesame Credit), state-administered (Rongcheng), professional (Hangzhou), and collaborative (Suzhou)—this study demonstrates that institutional logics function as generative mechanisms systematically shaping technology-society co-construction. We find that market, administrative, professional, and collaborative logics transform shared sociotechnical imaginaries into fundamentally different governance configurations across technical design, power distribution, norm construction, and subject formation. Critically, we theorize “differentiated algorithmic governmentalities” as a novel concept: while all systems cultivate self-regulating subjects through data-driven evaluation, they employ qualitatively distinct power mechanisms—seductive, disciplinary, expert, and networked governmentalities—operating through aspiration, compliance, professional identity, or coordination capacity. Our framework challenges both technological determinism and social constructivism, revealing that algorithmic effects emerge through institutionally specific mediations. This institutional diversity has profound implications for understanding digital governance and designing accountable algorithmic systems.