标题： 人工智能与城市研究中的跨学科知识：一种跨学科研究的框架 显示英文标题

标题： Intersectoral Knowledge in AI and Urban Studies: A Framework for Transdisciplinary Research

摘要： 跨学科方法在应对重大社会挑战方面变得越来越重要，尤其是在人工智能（AI）、城市规划和社会科学等复杂领域。 然而，有效验证和整合不同认识论和本体论视角的知识面临着重大困难。 本文提出了一种六维框架，用于评估和加强人工智能和城市研究中的跨学科知识有效性，该框架基于对2014年至2024年间最常被引用的研究的广泛分析。 具体而言，该框架根据本体论、认识论、方法论、目的论、价值论和价值化维度对研究方向进行分类。 我们的研究结果表明，以批判实在论（本体论）、实证主义（认识论）、分析方法（方法论）、结果主义（目的论）、认识论价值（价值论）和社会/经济价值化为主导。 也探讨了理想主义、混合方法和文化价值化等较少见的观点，以评估它们在丰富知识生产方面的潜力。 我们强调了早期职业研究人员和跨学科团队如何利用这一框架来调和不同的学科观点，并促进具有社会问责性的成果。 显示英文摘要

摘要： 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.