The field of gifted education has faced issues of equity for decades. Peters (2021) highlights a variety of systemic reasons traditional gifted identification processes may fail to equitably identify traditionally underrepresented students; however, at the core of Peter’s argument is a defense of the gifted and talented label. Common criticisms of gifted education include that it promotes fixed labels attached to opaque instructional practices or services that provide greater advantages to a privileged few (Grissom et al., 2019). The gifted label also attracts stereotypes and misconceptions relative to student behaviors and services. For instance, the label perpetuates the misconception that most students are gifted across academic domains rather than having specific areas of strengths and weaknesses (Lohman et al., 2008). As a result, gifted education services rarely meet the unique needs of the students being served, but rather provide a onesize-fits-all service—the antithesis of a specialized service. Dixson et al. (2021) proposed an approach to gifted education focused on maximizing learning, which involves short-term, malleable decisions using assessment data to immediately inform instruction rather than long-term labels. Essentially, these are existing strategies, primarily used in special education, to monitor progress with data-based decision making designed to support students academically. Three key services include (a) diagnostic labels only as needed, (b) push-in services from specialists ensuring students get instruction aligned with their needs, and (c) responsive services, such as Response to Intervention (RtI), that allow teachers to identify needs and modify instruction more effectively for a larger pool of (potentially or currently) highachieving students.

中文翻译：

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以资产为基础的决策以解决资优教育服务中的不平等问题

数十年来，资优教育领域一直面临着公平问题。Peters (2021) 强调了传统天才识别过程可能无法公平识别传统上代表性不足的学生的各种系统性原因；然而，彼得论点的核心是为天才和才华横溢的标签辩护。对天才教育的常见批评包括，它提倡贴在不透明的教学实践或服务上的固定标签，为少数特权阶层提供更大的优势（Grissom 等，2019）。天才标签还吸引了与学生行为和服务相关的刻板印象和误解。例如，该标签延续了这样一种误解，即大多数学生在学术领域都有天赋，而不是具有特定的优势和劣势领域（Lohman 等人，2008 年）。因此，资优教育服务很少满足被服务学生的独特需求，而是提供一刀切的服务——与专业服务相反。迪克森等人。(2021) 提出了一种专注于最大化学习的天才教育方法，该方法涉及使用评估数据而不是长期标签来立即通知教学的短期、可塑性决策。从本质上讲，这些是现有的策略，主要用于特殊教育，通过旨在支持学生学业的基于数据的决策来监控进度。三项关键服务包括 (a) 仅在需要时提供诊断标签，(b) 专家提供的推入式服务，确保学生获得符合其需求的教学，以及 (c) 响应式服务，例如干预响应 (RtI)，