{
  "abstract": "Practitioner-derived taxonomies of conversational artificial intelligence (AI) workflow errors show low inter-rater agreement among human coders, leaving open whether the instrument is ill-specified or the judgments are inherently difficult. We delivered a locked eight-category workflow-error taxonomy verbatim, under standardized conditions, to three frontier large language model comparators from distinct developer lineages, which coded a documented 45-incident error corpus. On the same 16 incidents coded by three human reviewer-authors, comparator category agreement was substantial (Fleiss {kappa}=0.625) against slight human agreement ({kappa}=0.155); across the full corpus it was stable ({kappa}=0.632), and a 10-category refinement did not reduce it ({kappa}=0.690). Severity and a claimed-verification flag remained only fair in both arms and on both samples. Substantial cross-system consistency provides a legibility signal consistent with recoverable category distinctions, but cannot separate instrument clarity from shared model priors, and is not a validation of any coding.",
  "abstract_shown": true,
  "affiliations": [
    "Xavier University of Louisiana"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "D. Austria"
    },
    {
      "name": "B. McCollister"
    },
    {
      "name": "J. E. Lindsey"
    },
    {
      "name": "M. Arowolo"
    },
    {
      "name": "M. Okon"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6kca7gxkw6mah.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.02.26362090",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.02.26362090v1.full",
  "id": "6kca7gxkw6mah",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.02.26362090v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "1804|Reliability and Agreement in Measurement"
    ]
  ],
  "openalex": "W7211963716",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.02.26362090v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6kca7gxkw6mah.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:57Z",
      "native_id": "10.64898/2026.09.02.26362090",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.02.26362090v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Cross-System Legibility of a Practitioner-Derived Workflow-Error Taxonomy for Conversational AI: A Three-Comparator Agreement Study",
  "title_text": "Cross-System Legibility of a Practitioner-Derived Workflow-Error Taxonomy for Conversational AI: A Three-Comparator Agreement Study",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:57Z",
  "url": "https://research.draconitic.com/paper/6kca7gxkw6mah",
  "venue": "medRxiv",
  "versions": []
}