将智能代理性别感知操作化：基础与指南
Operationalizing Perceptions of Agent Gender: Foundations and Guidelines
AI总结：
本文探讨智能代理性别感知的操作化方法，提出理论驱动的框架以提高研究严谨性和包容性，解决现有标准缺失的问题。
摘要：
智能代理、虚拟角色、社交机器人等的'性别'已成为人机交互研究中的核心问题。代理性别感知有助于解释用户态度和行为，但目前缺乏统一标准。本文通过系统综述明确了代理性别作为感知变量的定义、测量和操作方法，指出现有研究中三分之一未测量性别，规范模糊限制了结果理解与比较。本文提出理论驱动的元框架，提供操作清晰度和实践指导，以提升研究严谨性和包容性。
英文摘要：
The "gender" of intelligent agents, virtual characters, social robots, and other agentic machines has emerged as a fundamental topic in studies of people's interactions with computers. Perceptions of agent gender can help explain user attitudes and behaviours -- from preferences to toxicity to stereotyping -- across a variety of systems and contexts of use. Yet, standards in capturing perceptions of agent gender do not exist. A scoping review was conducted to clarify how agent gender has been operationalized -- labelled, defined, and measured -- as a perceptual variable. One-third of studies manipulated but did not measure agent gender. Norms in operationalizations remain obscure, limiting comprehension of results, congruity in measurement, and comparability for meta-analyses. The dominance of the gender binary model and latent anthropocentrism have placed arbitrary limits on knowledge generation and reified the status quo. We contribute a systematically-developed and theory-driven meta-level framework that offers operational clarity and practical guidance for greater rigour and inclusivity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
