While Large Language Models (LLMs) offer a solution to the scale-versus-depth dilemma in qualitative analysis, the paradigm of maximizing automation is fundamentally at odds with the interpretive nature of qualitative inquiry. We argue that effective Human-AI collaboration is not an automation problem, but an interdependence problem. This paper reframes the design of "co-data" systems through the lens of Interdependence Theory, proposing a formal framework to structure human-AI productive interdependence. The framework guides the selection of an appropriate Level of Automation (LoA) for different stages of the qualitative analysis process by assessing task risk and the cost of validation. We present a case study where this framework led to a deliberately interdependent workflow, fostering the calibrated trust necessary for rigorous analysis. We conclude by presenting three design principles that instantiate this framework, demonstrating how to leverage AI as a powerful partner while preserving the human researcher's irreplaceable role in the transformation process of meaning-making.

翻译：尽管大语言模型（LLMs）为质性分析中规模与深度的两难困境提供了解决方案，但最大化自动化的范式本质上与质性探究的诠释性特征相矛盾。我们认为，有效的人机协作并非自动化问题，而是互依性问题。本文通过互依性理论视角重构"协同数据"系统的设计，提出一个正式框架来构建人机高效互依。该框架通过评估任务风险与验证成本，指导在质性分析过程的不同阶段选择合适的自动化水平（LoA）。我们通过一项案例研究展示该框架如何催生出有意设计的互依工作流程，进而培养严谨分析所需的校准信任。最后，我们提出三项体现该框架的设计原则，阐明如何在保留人类研究者作为意义建构转化过程中不可替代角色的前提下，将AI塑造为强大的合作伙伴。