Beyond a Writer Label: Rich Context Shapes AI-Text Detector Scores

AI-text detector evaluations depend on how test passages are prompted. We ask whether a brief writer identity captures the response to a richly developed background. Across 150 Chinese literary-writing topics and five generators (4,500 texts), a one-sentence writer label changes detector scores little, while a detailed fictional background produces much larger shifts. The background also lowers scores more than the tested anti-AI-style directive: by 0.062 on AIGC-ZHv2's classifier scale and 0.098 corpus-rank units for Fast-DetectGPT. Two further biographies preserve the strategy-level separation. In a separate three-generator cohort, an independent second draw preserves the aggregate score-shift directions, and life-history facts carry the larger component response within the complete persona. A detailed description of a fictional town, with no assigned writer identity, then produces a reduction similar to that life-fact bundle across the 12 statistical outputs (median 0.264 versus 0.257 within the same experiment). Rich contextual information can therefore elicit large detector-score shifts without an explicit writer identity. Evaluations should distinguish brief labels from developed contexts and report their generator-specific responses.