Qualitative analysis of lexical hedges: EAP & AI

Evidano is an AI-powered qualitative data analysis platform that lets you reproduce the July 6, 2026 PLOS One finding that CASEC favors We V (that) while MICASE favors I V (that), and convert corpus diagnostics into classroom DDL materials.

The PLOS One diagnosis (July 6, 2026) shows a diagnostic I vs We pragmatic split across CASEC and MICASE, and the same pipeline (automatic transcription, concordance extraction, phrase-level coding) reproduces those diagnostics at scale.

• The PLOS study compared CASEC (696, 009 words; 279 events) with MICASE (1, 848, 364 words; 152 events) and found opposing phrasal preferences.
• The phrasal split: MICASE I V (that) = 2, 126 pmw vs CASEC I V (that) = 76 pmw; CASEC We V (that) = 889 pmw vs MICASE We V (that) = 87 pmw.
• Overall lexical-hedge frequency was CASEC 3, 592 pmw vs MICASE 3, 823 pmw (LL = -7.27, p < 0.01), a macro-level difference the study documents.

The PLOS One study (published July 6, 2026) found a striking I vs We split: MICASE heavily favors I V (that) while CASEC overuses We V (that).

Wang et al. compared a 696, 009-word Chinese Scholars’ Academic Spoken English Corpus (CASEC; 279 events) with MICASE (1, 848, 364 words; 152 events) and documented the phrasal mismatch that can affect reception in Anglo-American gatekeeping venues.

• Full study: PLOS One article (July 6, 2026).
• Why it matters: the phrasal mismatch can affect reception in Anglo-American gatekeeping venues; below we show how to reproduce the qualitative analysis and operationalize DDL with AI-enabled tools.

The authors compiled CASEC (696k words, 279 events; 2018–2025) and compared it to MICASE (1.85M words) and targeted 10 high-frequency lexical verbs (see, think, assume, consider, etc.).

• Automatic ASR then manual correction: the study used OpenAI Whisper large-v3 for initial transcripts, manual correction in ELAN, and final verification, yielding word-level agreement 98.4%.
• Concordance retrieval (AntConc) produced 9, 567 lines; the authors manually coded hedge function with Cohen’s κ = 0.85.
• Normalization to pmw and log-likelihood testing determined significance; phrasal pattern extraction focused on subject type × complement (I V that, We V that, It V that, N V that).

This two-week checklist reproduces the PLOS-style qualitative analysis on your corpus using the same diagnostic steps.

• 1) Collect 20–50 recorded talks or 50–200 transcripts and upload to Evidano.
• 2) Auto-transcribe with a custom dictionary, then review edits in the editor; enable PII redaction if needed.
• 3) Extract target lexical verbs and generate concordances; export 9–10k lines like the study’s sample.
• 4) Auto-tag subject types (I, we, it, inanimate) and complement frames; produce pmw tables and run LL tests.
• 5) Curate 20 contrastive concordance lines for I vs We worksheets and let Evidano generate guided questions.
• 6) Run AI avatar Q&A role-plays for presenters to practice switching I/We strategies and capture recordings for feedback.
• 7) Produce a one-page pedagogical brief and export slides/visuals for stakeholders.

The PLOS One diagnosis (July 6, 2026) of a clear I vs We pragmatic split is a concrete example of how corpus-driven qualitative analysis uncovers high-leverage pedagogical targets.

Replicate the study’s diagnostic steps in days, not months, by combining automated transcription, concordance extraction, phrase-level coding, and AI-assisted worksheet generation. Start a pilot at Try Evidano for free to ingest your corpus, run the qualitative analysis, and produce classroom-ready DDL materials.