Adaptive Merit Framework: Merit-Anchored Fairness via SES Correction

College admissions systems worldwide continue to face a structural tension between meritocracy and equity. Conventional fairness interventions--affirmative action, categorical quotas, and proxy-based targeting--often rely on coarse indicators (e.g., race or region), operate within fixed quotas that induce zero-sum trade-offs, and lack transparent decision rules. This paper introduces the Adaptive Merit Framework (AMF), a policy-engineered mechanism that recognizes latent potential while preserving merit-based thresholds. AMF integrates three components: (1) a merit-anchored architecture in which conditional admits must exceed the same threshold as regular admits, (2) a dynamic threshold anchored to the raw score of the last regular admit, and (3) direct, continuous SES measurement verified through administrative data. Empirical validation using the full PISA 2022 Korea dataset (N=6,377) shows that AMF identifies 4, 6, and 9 additional admits under alpha = 5, 10, and 15 respectively (0.06-0.14% of cohort). Population-weighted estimates using OECD sampling weights suggest that the real-world scale of conditional admits is modestly larger than the raw sample counts, yielding approximately 491, 603, and 760 additional admits under alpha = 5, 10, and 15. All conditional admits exceed the merit threshold by 0.16 to 6.14 points, indicating that AMF recognizes suppressed performance rather than relaxing standards. Beyond SES-based corrections, AMF provides a design template for unified admissions architectures that replace fragmented equity tracks and support multi-dimensional evaluation frameworks.