志愿研究的AI转向：知识生产的新范式
The AI Turn in Volunteer Research: A New Paradigm of Knowledge Production
提交：2025-02-05录用：2025-05-15出版：2025-06-30
社会工作与志愿服务, 2025, 2(1), 1-24; https://doi.org/10.58244/jswv.263982
摘要：数字文明时代的到来，深刻重塑着人类公益实践的组织形态，同时对旨在理解这一实践的社会科学知识生产范式发起了历史性提问。本研究直面传统志愿社会学在数据基础、分析方法和理论生成层面所面临的三重困境，首次提出并系统论证了该领域一场自觉的“人工智能转向”“计算范式革命”的必然路径与深远意涵。研究指出，这场革命的核心并非技术工具的简单移植，而是一场从本体论【更多...】
关键词：计算化志愿社会学；多层次融合框架；理论形式化；大语言模型；生成式智能体；多智能体仿真
摘要
摘 要：
数字文明时代的到来，深刻重塑着人类公益实践的组织形态，同时对旨在理解这一实践的社会科学知识生产范式发起了历史性提问。本研究直面传统志愿社会学在数据基础、分析方法和理论生成层面所面临的三重困境，首次提出并系统论证了该领域一场自觉的“人工智能转向”“计算范式革命”的必然路径与深远意涵。研究指出，这场革命的核心并非技术工具的简单移植，而是一场从本体论、认识论到方法论的深刻重构。在本体论层面，它推动研究视角从对静态“社会变量”的测量，转向对动态“社会过程”与复杂“关系系统”的生成性理解。在认识论层面，它构建了一个贯通微观行为序列、中观网络结构与宏观系统生态的多层次可计算融合框架，首次实现了对社会资本、制度嵌入、动机演化等核心社会学概念的形式化操作与机制化建模。在方法论层面，它革命性地重构了理论与经验的关系：深度学习、图神经网络与多智能体仿真等人工智能模型，超越了传统“假设—验证”工具的角色，而是演变成能够从海量数字痕迹中自主发现模式、检验机制乃至生成新理论的“社会知识实验室”。志愿研究的知识生产模式得以实现三重跃迁：从“截面描述”走向“全程追溯”，从“属性归因”走向“关系建模”，从“解释世界”的单一目标拓展至兼具“预测情境”与“设计干预”的“知行合一”新科学。本研究的论述，不仅为数字化条件下的志愿行为研究、组织治理与政策优化提供了一套具有高度可操作性的系统性行动纲领，其更深层的旨趣在于，以志愿社会学为典型场域，探索并阐明人工智能与社会学理论深度互构所可能催生的、更具普遍意义的中国社会科学研究新范式。这一范式的成熟，会成为数字时代把握社会运行规律、推动治理现代化并丰富人类自我理解所不可或缺的智识基石。
关键词：计算化志愿社会学；多层次融合框架；理论形式化；大语言模型；生成式智能体；多智能体仿真
Abstract:
The advent of the digital civilization era is profoundly reshaping the organizational forms of human philanthropic practices, while also posing a historic challenge to the knowledge production paradigm of social sciences that seek to understand these practices. This paper directly confronts the triple dilemmas faced by traditional volunteer sociology in terms of data foundation, analytical methods, and theory generation. It proposes and systematically argues, for the first time in this field, the inevitable path and profound implications of a conscious“artificial intelligence turn” or “computational paradigm revolution”. The study points out that the core of this revolution is not a simple transplantation of technical tools, but a profound reconstruction from ontology, epistemology, to methodology. At the ontological level, it shifts the research perspective from measuring static “social variables” to the generative understanding of dynamic “social processes” and complex“relational systems”. At the epistemological level, it constructs a multi-level computable integration framework connecting micro-behavioral sequences, meso-level network structures, and macro-system ecologies, achieving for the first time the formalization and mechanistic modeling of core sociological constructs such as social capital, institutional embeddedness, and motivational evolution. At the methodological level, it revolutionizes the relationship between theory and experience: artificial intelligence models such as deep learning, graph neural networks, and multi-agent simulations transcend the traditional role of “hypothesis-testing” tools, evolving into “social knowledge laboratories” capable of autonomously discovering patterns, testing mechanisms, and even generating new theories from massive digital traces. Consequently, the knowledge production model of volunteer research achieves three leaps: from “cross-sectional description” to “full-process tracing”, from “attribute attribution” to “relational modeling”, and from the single goal of “explaining the world” to the new science of “integrating knowledge and action” that combines “predicting scenarios” and “designing interventions”. The discourse of this paper not only provides a highly operable and systematic action plan for the study of volunteer behavior, organizational governance, and policy optimization under digital conditions, but its deeper aim is to use volunteer sociology as a typical field to explore and elucidate a new paradigm for Chinese social science research with broader significance, one that can be catalyzed by the deep mutual construction of artificial intelligence and sociological theory. The maturation of this paradigm will be an indispensable intellectual cornerstone for grasping the laws of social operation, promoting governance modernization, and enriching human self-understanding in the digital age.
Keywords: Computational volunteer sociology; Multi-level integration framework; Theoretical formalization; Large language models; Generative agents; Multi-agent simulation
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作者
作 者 / Authors：
张网成，北京师范大学社会学院教授。
余卓尔，香港城市大学社会及行为科学系，硕士研究生。
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