安抚算法:零工工作者算法反馈策略

Pacifying the Algorithm:Gig Worker Algorithm Feedback Activity

随着算法技术在零工经济中的广泛使用,零工工作者呈现出特有的安抚算法策略。然而,当前安抚算法研究“尚处于黑箱”。本研究应用扎根理论,基于39名零工工作者的算法反馈实践,重新界定安抚算法的概念,提出安抚算法是算法管理模式下,零工工作者为获取平台资源,计划并实施符合算法规定的额外劳动,以开展自我管理的过程。进一步地,以行动者视角构建了“分析算法—安抚算法—寻求反馈—后续安抚”的零工工作者安抚算法四阶段过程模型;基于人机互动视角搭建了算法机制与安抚算法“诱发—应答—强化”的动态交互框架。本研究为后续开展实证研究奠定理论框架,为促进平台算法管理与零工工作者间良性互动提供有益借鉴。

Algorithmic technology has become an integral part of the gig economy,shaping the way gig workers interact with platforms and secure opportunities.Gig workers have developed unique strategies for pacifying algorithms.However,the current research on pacification algorithms is still in the black box.Drawing from grounded theory and the algorithm feedback practices of 39 gig workers,this article conducts three research projects.Firstly,this research redefines pacification algorithms as the process through which gig workers plan and execute additional labor to meet algorithmic requirements.This strategic approach allows them to access platform resources,ultimately fostering self-discipline.The study identifies and summarizes four fundamental conditions that underlie the concept of pacification algorithms.Furthermore,it presents a comprehensive four-stage process model from the perspective of gig workers,which encompasses the stages of"analysis algorithm,""pacification algorithm,""feedback-seeking,"and"follow-up pacification."This framework provides a systematic understanding of how gig workers engage with algorithms throughout their freelance endeavors.Additionally,the research introduces a dynamic interaction framework,employing the"elicit-response-reinforcement"model,to illustrate the evolving relationship between the algorithmic mechanism and the pacifying algorithm.Grounded in principles of humancomputer interaction,this framework elucidates how gig workers and algorithms co-evolve,adapt,and influence each other within the gig economy ecosystem.This study not only contributes a theoretical framework for future empirical research but also offers valuable resources to enhance the positive interaction between platform algorithm management and gig workers.