{
  "title": "Ento-Linguistics: Language, Ambiguity, and Scientific Communication in Entomology: How Terminology Networks Shape Understanding of Insect Biology (And Vice-Versa)",
  "version": "v1",
  "doi": "10.5281/zenodo.19574117",
  "doi_url": "https://doi.org/10.5281/zenodo.19574117",
  "zenodo_record": "https://zenodo.org/records/19574117",
  "record_id": "19574118",
  "publication_date": "2026-04-15",
  "resource_type": {
    "title": "Publication",
    "type": "publication"
  },
  "creators": [
    {
      "name": "Friedman, Daniel Ari",
      "affiliation": "Active Inference Institute",
      "orcid": "0000-0001-6232-9096"
    },
    {
      "name": "Chambers, Tucker Cahill",
      "affiliation": null,
      "orcid": "0009-0008-3793-7872"
    }
  ],
  "description": "Scientific language does not merely describe biological phenomena; it actively constitutes the generative models through which researchers parse complex systems. This paper makes three core contributions to understanding— and correcting—the epistemic consequences of this constitutive role. First, we introduce a six-domain Ento- Linguistic framework that decomposes the terminological landscape of insect research into analytically tractable themes, isolating domains where anthropomorphic language most severely distorts causal modeling. Second, we de- velop an open-source computational pipeline that integrates automated term extraction, co-occurrence network construction, and information-theoretic ambiguity scoring with principles from Active Inference and Complex Systems Theory. Third, we propose and validate four evidence-based meta-standards—Clarity, Appropriateness, Consistency, and Evolvability (CACE)—as a formalized protocol for lexical engineering. Analysis of a corpus encompassing 369 entomological publications (48787 tokens; 7105 unique token types; Type–Token Ratio 0.1456) extracts 888 candidate terms (with 261 assigned to specific semantic domains across 6 conceptual clusters linked by 9 weighted relationships). The resulting terminology networks display strong modularity alongside systematic cross-domain bridging—most prominently in the Power and Labor domain, where 43 bridging terms generate extensive semantic bleed-over into adjacent domains. Terms such as “queen” (241 occurrences), “worker” (269), and “caste” (121) implicitly impose hierarchical control topologies onto biological structures that are funda- mentally stigmergic and decentralized. Across all 261 domain-assigned terms, 16.9% exhibit context-dependent semantic drift, demonstrating how conceptual constructs like “individuality” span multiple biological scales and consequently blur the formal systemic boundaries (Markov Blankets) required for mathematically rigorous mod- eling. The accompanying fully reproducible computational pipeline provides the quantitative analytical tools necessary for a more self-aware and epistemically rigorous scientific practice. All code and data are available at https://github.com/docxology/ento_linguistics.",
  "keywords": [],
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  "related_resources": [],
  "github_repo": "",
  "source": "zenodo-only",
  "checked_at": "2026-06-30T23:26:10Z",
  "domain": "Entomology",
  "type": "Paper",
  "methods": [
    {
      "name": "Field observation and behavioral assays",
      "description": "Applied field observation and behavioral assays approach"
    },
    {
      "name": "Population genetics analysis",
      "description": "Applied population genetics analysis approach"
    }
  ],
  "key_findings": [
    "Six-domain Ento-Linguistic framework, open-source corpus pipeline (term extraction, co-occurrence networks, semantic entropy), and CACE meta-standards for lexical engineering in entomology."
  ],
  "related_papers": [
    "2016_AntGenetics",
    "2016_ForagingGene",
    "2017_MutAnts"
  ]
}