Small and Medium Enterprises (SMEs) and micro-enterprises form the economic backbone of the global construction sector, accounting for over 97% of active industrial stakeholders and generating more than half of the industry\'s total value-added output. Despite this overwhelming demographic and economic presence, foundational project management (PM) literature and professional bodies of knowledge remain calibrated for environments of corporate abundance — a condition this study terms the \"Academic-Industrial Orientation\" (AIO). This study executes a rule-governed Directed Qualitative Content Analysis (DQCA) across a corpus of 769 coded meaning units drawn from Harold Kerzner\'s Project Management: A Systems Approach to Planning, Scheduling, and Controlling (12th ed.) and the Project Management Institute\'s A Guide to the Project Management Body of Knowledge (PMBOK® Guide) (8th ed., 2025). The systematic audit empirically confirms a severe \"Large-Firm Bias,\" with indicators of Enterprise-Scale Orientation (ESO) capturing between 68% and 84% of coded distributions across all operational tiers. Drawing on Institutional Theory and Resource Dependence Theory, this paper deconstructs how mainstream frameworks implicitly mandate specialized role segregation, heavy transaction-cost mitigation, and zero-data-friction administrative overhead — bureaucratic burdens that smaller firms simply cannot sustain financially or structurally. This structural mismatch produces what the study calls the \"Newly Graduated Engineer\'s Paradox,\" trapping entry-level practitioners in a hazardous \"Task-Execution Trap\" where textbook strategies actively disrupt site velocity and exhaust finite cognitive capacity. To close this reality gap, the paper introduces an SME-Adaptive Engineering Management Framework, featuring a lightweight Project Tailoring Checklist, an administrative decoupling protocol, and an interactive friction calculator. A dual-engine Competency Framework addresses early-career identity shock by reorienting professional capabilities around look-ahead production velocity, relational network governance, and Applied AI autonomy. These tools demonstrate that while standard project management administration is rigidly scale-dependent, core engineering management mathematics remain fundamentally scale-neutral.