Position: Bridge Human Interpretation and Machine Representation With Explicit Specification For Qualitative Data Analysis In LLM Era

Large language models (LLMs) are increasingly used in qualitative data analysis, yet the field lacks a shared way to state what kinds of process LLM-based pipelines intend to produce. This position paper proposes an explicit specification perspective: separating meaning-making from modeling, and making both visible as part of the analytic. We introduce a 4×4 landscape that crosses levels of meaning-making with levels of modeling, and use it to situate and compare qualitative outputs across both human-led studies and LLM-assisted workflows. A structured analysis of prior work suggests that many current LLM pipelines emphasize surface organization and static representations, with fewer systems making explicit commitments to richer causal or dynamical models. We demonstrate that the landscape can be applied consistently through strong agreement in independent labeling, including an LLM-based annotation pass. We conclude with a research agenda for LLM-assisted qualitative analysis focused on explicit level selection, evidence-linked outputs, and governance mechanisms aligned with the strength of semantic and representational claims.