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In the Harvard experiment, task quality was evaluated using subjective rubrics scored by human graders assessing writing clarity, persuasiveness, logical structure, and creative breadth. On these specific dimensions, generative language models excel at synthesizing standard corporate rhetoric, framing arguments with polished syntax, and organizing ideas into structured bullet points. A junior or less capable consultant who struggles with professional business writing receives an immediate boost from an automated draft: $\text{Quality}_{\text{measured}} = w_1 \cdot \text{Fluency} + w_2 \cdot \text{Structure} + w_3 \cdot \text{Domain Validity}$. When grading rubrics heavily weight surface fluency and coherence ( $w_1$ + $w_2$ $\gg$ $w_3$ ), low-skill participants appear to leap forward. However, this…

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