如何理解新时期市民身份认同建构新特征、有效疏导新老市民身份认同矛盾，已成为当前城市治理的核心问题，亦为实现新型城镇化人本目标重要前提。本研究聚焦城镇化治理现实需求，建立身份认同多层交互建构模型，从人口、经济、文化三个维度剖析新型城镇化进程中的身份认同建构机制。研究综合运用全国代表性样本调查、社会经济统计、社交媒体等多源数据，以及实验、调查分析、大数据分析等前沿方法，对身份认同建构机制，尤其是新老市民建构的交互性进行科学考察，并在此基础上提炼行之有效的治理工具，为应对城镇化治理挑战、实现“人的城镇化”提供政策借鉴。本课题在着眼点上，结合国家治理现实需要，兼顾基础研究和治理需求的对应性；理论上，突破偏重流入人口的研究局限，以交叉学科视角构建理论模型；方法上，将政治经济学、社会心理学和语言学研究方法引入政策研究，定性与定量方法结合；数据上，运用多源数据优势，大数据与小数据结合综合论证。

A primary challenge of governance in urbanization is to understand new patterns in the identity construction in the new era and to alleviate the identity conflicts between the new and original citizens. It is a fundamental issue for achieving the humanist goal of the "new urbanization." This project aims at these immediate demands of urban governance and constructs a multi-level interactive adjustment model to analyze the mechanisms of identity construction in the three core dimensions (demography, economy, and culture) of modern Chinese cities. The study combines multiple data sources (surveys based on nationally representative samples, socioeconomic statistics, social media data, etc.) and cutting-edge methods (experimental methods, survey analysis, big data analysis, etc.) to scientifically examine the identity mechanisms, especially its interactive nature between the native and new citizen groups. Furthermore, the project tests a variety of policy tools for handling the challenge and provides evidence-based policy suggestions. There are four major contributions to the project: 1. Aiming to the operational demands of modern governance, seeking a balance between academic research and governing practices; 2. In terms of theory, transcending the limitations of the existing perspectives and building up the analytic framework based on an interdisciplinary view; 3. In terms of methodology, introducing economic, psychological, and linguistic methods into public policy analysis and combining the quantitative and qualitative approaches; 4. Regarding the data, combining multiple data sources and taking the strength of both small and big data.