This paper re-examines the concept of segmentation in Quantitative Ethnography (QE), a topic that has historically confused new practitioners. The original formulation of segmentation, drawing analogies from poetry and employing an Initial Letter Convention (e.g., Line vs. line), created unnecessary conceptual hurdles. This work argues for retiring these confusing conventions in favor of a more intuitive and theoretically grounded framework: specifically, replacing the terms stanza and conversation with the concepts of window and horizon, respectively, to better align with contemporary theories of discourse and common ground. A window represents the immediate temporal context a person attends to, while a horizon defines the total perceptual field from which that window is drawn. This reframing is particularly vital for handling complex, multimodal data where different data streams contribute unequally to a participant’s perceived context. As a result, this paper advocates for reconceptualizing the construction of a QE analysis into four distinct modeling stages—structural, content, discourse, and semantic—as this framework offers a more transparent and defensible approach to building and justifying QE models.

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Models All the Way Down

  • David Williamson Shaffer,
  • A. R. Ruis

摘要

This paper re-examines the concept of segmentation in Quantitative Ethnography (QE), a topic that has historically confused new practitioners. The original formulation of segmentation, drawing analogies from poetry and employing an Initial Letter Convention (e.g., Line vs. line), created unnecessary conceptual hurdles. This work argues for retiring these confusing conventions in favor of a more intuitive and theoretically grounded framework: specifically, replacing the terms stanza and conversation with the concepts of window and horizon, respectively, to better align with contemporary theories of discourse and common ground. A window represents the immediate temporal context a person attends to, while a horizon defines the total perceptual field from which that window is drawn. This reframing is particularly vital for handling complex, multimodal data where different data streams contribute unequally to a participant’s perceived context. As a result, this paper advocates for reconceptualizing the construction of a QE analysis into four distinct modeling stages—structural, content, discourse, and semantic—as this framework offers a more transparent and defensible approach to building and justifying QE models.