Ento-Linguistics: Language, Ambiguity, and Scientific Communication in Entomology: How Terminology Networks Shape Understanding of Insect Biology (And Vice-Versa)

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/entoₗinguistics.