{
  "abstract": "Large language model embeddings give any text coordinates in a semantic space, where the cosine between two coordinates indexes their similarity. Standard qualitative practice cannot quantify such closeness: open-ended answers become nominal variables, which represent only difference under dummy codes. Psychometrics obtains such closeness from responses, as the correlations among a scale's items. Whether the cosine can substitute for the correlation is therefore open. We tested it against established findings in open data, each with an agreed structure: the Big Five, PANAS in two languages, Schwartz's values, Pineo-Porter occupational prestige, and the ISEAR and Kjell narratives. Linguistic structure alone did not reproduce the psychometric structure. The item texts recovered part of it, and none of it where a relation carries a sign. They fall far short of a random split-sample ceiling. Read from the other side, that is a finding about questionnaires: a scale's wording does not carry what its respondents give it. Embeddings supply a semantic layer between raw language and conventional coding, where the material that neither psychometrics nor hand coding reaches belongs. A category label—an occupation, a diagnosis, a brand—becomes coordinates rather than a dummy variable. An open-ended answer becomes coordinates without a coder. Cut into segments, a long answer becomes a path, along which departures, returns, and drifts can be drawn and tested. What any of it means, however, stays with the analyst. We supply the conceptual background, an R package, and a tutorial: no training, no Python.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Toshizumi Muta"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8gw8kgw5v3w3e.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/6hxsw_v2",
  "fulltext_url": null,
  "id": "8gw8kgw5v3w3e",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/6hxsw_v2/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Computational Linguistics"
    ],
    [
      "bepress",
      "Linguistics"
    ],
    [
      "bepress",
      "Narrative Research"
    ],
    [
      "bepress",
      "Quantitative Methods"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "bepress",
      "Social and Personality Psychology"
    ],
    [
      "bepress",
      "Statistical Methods"
    ],
    [
      "openalex",
      "3300|Computational and Text Analysis Methods"
    ]
  ],
  "openalex": "W7213912649",
  "pdf_url": "https://osf.io/6hxsw/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8gw8kgw5v3w3e.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:11Z",
      "native_id": "6hxsw",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/6hxsw_v2/"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "bepress:Statistical Methods",
      "subfield": "statistics"
    }
  ],
  "title": "Embedding qualitative data in LLM semantic space: A tutorial on conceptualization, measurement, and validation",
  "title_text": "Embedding qualitative data in LLM semantic space: A tutorial on conceptualization, measurement, and validation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:11Z",
  "url": "https://research.draconitic.com/paper/8gw8kgw5v3w3e",
  "venue": "PsyArXiv",
  "versions": []
}