明星工作者溢出效应过程机制研究:基于非流动与流动视角的整合框架
Science & Technology Progress and Policy（2023）
中国人民大学
Cited 0|Views12
Abstract
已有明星工作者研究大多忽视了明星工作者的非流动性和流动性特点以及溢出效应过程机制,未解析在非流动和流动情境下明星工作者如何对其他主体产生溢出作用.通过回顾已有文献,揭示非流动和流动视角下明星工作者的溢出效应并构建整合框架.首先,梳理明星工作者内涵发展脉络及类型,明晰明星工作者溢出效应内涵.其次,阐述非流动视角下溢出效应过程机制,即基于社会比较理论的认知路径和情绪路径,基于社会网络理论的信号路径、资源路径和心理路径;阐述流动视角下溢出效应的过程机制,即基于人力资本理论的知识路径、社会资本理论的社会关系路径以及两者共同作用的成本—收益路径.再次,构建非流动视角和流动视角下明星工作者溢出效应的整合框架.最后,提出未来应关注的重点方向,可为明星工作者研究和新时代人才管理实践提供重要参考.
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Key words
Star Performers,High Performers,Spillover Effect,Talent Management,Integrative Framework
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Pretraining has recently greatly promoted the development of natural language processing (NLP)We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performanceWe propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generationThe model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in ChineseExperimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performanceUpload PDF to Generate Summary
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