构建六维框架，打通AI与城市研究的跨学科知识壁垒
Intersectoral Knowledge in AI and Urban Studies: A Framework for Transdisciplinary Research
- 从本体、认识论等六个维度划分研究立场
- 发现主流研究多倾向批判实在论与功利主义视角
- 帮助青年学者和跨学科团队弥合观点分歧
跨学科研究日益成为应对重大社会挑战的关键，尤其在人工智能、城市规划与社会科学等复杂领域。本文基于2014至2024年高被引研究的广泛分析，提出一个六维框架，用于评估与强化人工智能与城市研究中的跨学科知识有效性。该框架涵盖本体论、认识论、方法论、目的论、价值论及价值实现六个维度，对研究取向进行分类。研究发现，主流立场多为批判实在论（本体论）、实证主义（认识论）、分析方法（方法论）、后果主义（目的论）、认知价值（价值论）以及社会经济价值化。同时探讨了理想主义、混合方法与文化价值化等较少见立场在丰富知识生产中的潜力。本文强调，早期研究人员与跨学科团队可借助此框架协调不同学科视角，推动更具社会责任感的研究成果。
原文摘要 · Abstract (English)
Transdisciplinary approaches are increasingly essential for addressing grand societal challenges, particularly in complex domains such as Artificial Intelligence (AI), urban planning, and social sciences. However, effectively validating and integrating knowledge across distinct epistemic and ontological perspectives poses significant difficulties. This article proposes a six-dimensional framework for assessing and strengthening transdisciplinary knowledge validity in AI and city studies, based on an extensive analysis of the most cited research (2014--2024). Specifically, the framework classifies research orientations according to ontological, epistemological, methodological, teleological, axiological, and valorization dimensions. Our findings show a predominance of perspectives aligned with critical realism (ontological), positivism (epistemological), analytical methods (methodological), consequentialism (teleological), epistemic values (axiological), and social/economic valorization. Less common stances, such as idealism, mixed methods, and cultural valorization, are also examined for their potential to enrich knowledge production. We highlight how early career researchers and transdisciplinary teams can leverage this framework to reconcile divergent disciplinary viewpoints and promote socially accountable outcomes.
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