将大模型视为颠覆认知边界的智能怪物，揭示其对组织知识的双重影响。
A time for monsters: Organizational knowing after LLMs
- 用哈拉维式'怪物'概念理解大模型的混合性与边界跨越性
- 发现大模型通过统计推断构建远域/近域、表面/深层类比扩展知识
- 提出三重挑战：认知方式变革、对话式验证需求、主体性重新分配
大型语言模型（LLMs）正重塑组织认知，动摇了表征性与实践性视角的认知基础。本文将LLMs概念化为哈拉维式的‘怪物’——兼具杂交性与跨界性的存在，瓦解既有分类的同时开辟新的探究路径。聚焦类比作为知识生成的核心驱动力，分析大模型如何通过大规模统计推理建立联系。考察其在表面/深层类比与近域/远域维度上的运作，揭示其拓展组织认知的能力及带来的认知风险。在此基础上，识别出与这类认知‘怪物’共存的三大挑战：探究方式的转型、对话式验证需求的增长、以及能动性的再分配。通过凸显与大模型共同认知的纠缠动态，论文将组织理论从人类中心的认知范式中拓展出来，呼吁重新关注智能时代知识的生成、验证与行动机制。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are reshaping organizational knowing by unsettling the epistemological foundations of representational and practice-based perspectives. We conceptualize LLMs as Haraway-ian monsters, that is, hybrid, boundary-crossing entities that destabilize established categories while opening new possibilities for inquiry. Focusing on analogizing as a fundamental driver of knowledge, we examine how LLMs generate connections through large-scale statistical inference. Analyzing their operation across the dimensions of surface/deep analogies and near/far domains, we highlight both their capacity to expand organizational knowing and the epistemic risks they introduce. Building on this, we identify three challenges of living with such epistemic monsters: the transformation of inquiry, the growing need for dialogical vetting, and the redistribution of agency. By foregrounding the entangled dynamics of knowing-with-LLMs, the paper extends organizational theory beyond human-centered epistemologies and invites renewed attention to how knowledge is created, validated, and acted upon in the age of intelligent technologies.
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