5,266 Matching Annotations
  1. Mar 2026
  2. glassmanlab.seas.harvard.edu glassmanlab.seas.harvard.edu
    AbstractExplorer: Leveraging Structure-Mapping Theory to Enhance Comparative Close Reading at Scale
    200
    1. elglassman 25 Mar 2026
      Using a two-tailed Mann-Whitney U Test, we found that participants who reported their lowest perceived cognitive load when all three features were enabled had significantly lower NFC than participants who reported their lowest cognitive load level when skimming with no features enabled—in the baseline interface (p=0.03).

      sentence describing how analysis was performed on data collected by the authors of this paper

      ai-pending data analysis
    4. elglassman 25 Mar 2026
      For simplicity of analysis, we denote participants with NFC scores above the overall participants' median NFC of 5.42 (IQR = 0.583) as higher NFC, and lower NFC otherwise.

      sentence describing how analysis was performed on data collected by the authors of this paper

      ai-pending data analysis
    5. elglassman 25 Mar 2026
      To contrast participants' gaze patterns in each condition, we used a Tobii Pro Spark eye-tracker placed below the desktop monitor used by all subjects; Tobii Pro Lab software recorded each participant's gaze over time in each condition.

      sentence describing how analysis was performed on data collected by the authors of this paper

      ai-pending data analysis
    6. elglassman 25 Mar 2026
      We collected 80 sentences from our abstracts dataset labeled by our system as "Methodology/Contribution." Participants viewed the same 80 sentences in each condition—often with a different subset of sentences initially visible due to ordering changes—but only had two minutes to look at them in each condition.

      sentence describing how analysis was performed on data collected by the authors of this paper

      ai-pending data analysis
    7. elglassman 25 Mar 2026
      After obtaining an expanded set of high-level chunk labels, we assign them to each of the sentence chunks by using LLMs in a multiclass classification few-shot learning task, with the initial labels and assignment as examples (see prompt used in Appendix D.3).

      sentence describing how analysis was performed on data collected by the authors of this paper

      ai-pending data analysis
    8. elglassman 25 Mar 2026
      Then, we segment sentences within each aspect into grammarpreserving chunks (see prompt used in Appendix D.2). This results in grammatically coherent chunks that are the basis of structure patterns. After identifying chunk boundaries, we again prompt an LLM to generate labels for chunks in a human-in-the-loop approach: starting from an initial set of labels for chunk roles, when a new label is generated, a researcher from the research team examines the new label and merges it with existing labels if appropriate, controlling for the total number of labels.

      sentence describing how analysis was performed on data collected by the authors of this paper

      ai-pending data analysis
    9. elglassman 25 Mar 2026
      We process this data in a three-stage pipeline (Figure 6). In the first stage, Sentence Segmentation and Categorization, abstracts are split into individual sentences using the NLTK package, and each sentence is classified into one of the five pre-defined aspects as listed in Section 4.1.1. Classification is performed by prompting an LLM (see prompt used in Appendix D.1) with the sentence and its full abstract.

      sentence describing how analysis was performed on data collected by the authors of this paper

      ai-pending data analysis
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    glassmanlab.seas.harvard.edu/papers/abstractexplorer.pdf