Admissions systems in many countries struggle to balance merit-based selection with equity objectives. Most existing approaches--categorical quotas, fragmented equity tracks, and opaque adjustments--lack transparent decision rules and operational coherence. This paper introduces the Adaptive Merit Framework (AMF), a mechanism-based architecture that combines an individual-level SES correction rule with a structured decision pipeline. AMF operates under a non-displacement constraint: regular admissions remain determined entirely by raw merit scores, and only applicants whose corrected performance exceeds the same threshold qualify as conditional admits. The framework is operationalized through a five-stage decision spine--input definition, indicator aggregation, equity calibration via a single parameter alpha, batch execution, and irreversible closure--eliminating institutional discretion throughout. An empirical application using PISA 2022 Korea data (N = 6,377) shows that AMF identifies 4-9 additional candidates exclusively from the bottom half of the SES distribution, all above the merit threshold, expanding admissions by fewer than 0.15% of the cohort. The results demonstrate that rule-based correction can recover suppressed high-merit individuals without displacing standard admits, providing a transparent and scalable alternative to discretionary equity interventions. Keywords: Mechanism design, Decision architecture, University admissions, Equity-efficiency tradeoff, Socioeconomic correction, Non-displacement

翻译：许多国家的招生体系在平衡择优选拔与公平目标方面面临挑战。现有的大多数方法——如分类配额、分散的公平通道和不透明的调整——缺乏透明的决策规则和操作连贯性。本文提出自适应择优框架（AMF），这是一种基于机制的架构，将个体层面的社会经济地位校正规则与结构化决策流程相结合。AMF在非替代约束下运行：常规录取完全由原始成绩决定，只有校正后表现超过相同阈值的申请者才有资格作为条件录取生。该框架通过五阶段决策核心实现操作化——输入定义、指标聚合、通过单一参数α进行公平校准、批次执行和不可逆终止——在整个过程中消除了机构的自由裁量权。使用PISA 2022韩国数据（N = 6,377）的实证应用表明，AMF能额外识别出4至9名完全来自社会经济地位分布下半区的候选者，且其成绩均高于择优阈值，使录取人数增加不到同届学生的0.15%。结果表明，基于规则的校正能够发掘被抑制的高潜力个体，且不会替代标准录取生，为自由裁量式的公平干预措施提供了一种透明且可扩展的替代方案。关键词：机制设计，决策架构，大学招生，公平效率权衡，社会经济地位校正，非替代性