Fields Without Minds: Can Distributed Order Help Explain the Emergence of Language Intelligence?

Contemporary language models acquire useful world-related regularities from corpora generated by many contributors, without possessing the first-person experiences that originally informed much of those texts. This fact is neither a recent discovery nor a proof of a collective mind: distributional semantics, latent semantic analysis, emergent communication, neural language modeling, and large-scale pretraining provide a long scientific lineage. Fields Without Minds asks a narrower question: can a precisely defined relational Field representation improve the identification, preservation, and causal qualification of organization as information passes from social experience into shared language, learned models, and situated behavior? Six relevant authorial preprints delimit the proposed Field programme without establishing scientific priority over earlier distributed theories. Taniguchi and colleagues' Collective World Model provides a substantial contemporary architectural comparator, not the historical origin of the collective-learning idea. The manuscript introduces a typed candidate representation, separates statistical reproduction from semantic, relational, and causal reconstruction, and critically synthesizes seven exploratory analytic and synthetic studies. Those studies illustrate information-loss limits, topology-dependent invariants, incompatibility between learned conventions, ambiguous translation, false relational witnesses, information synergy, and intervention-based causal distinctions. Conventional information-theoretic, graph, translation, neural, and causal baselines match the reported Field-labelled analyses; unarchived computational logs restrict independent reproducibility. The contribution is a falsifier-first cross-regime qualification programme, not evidence for a novel physical Field, a conscious collective mind, or an established advantage over existing mathematics. Its central claim remains conditional: distributed order may matter for explaining language intelligence, but a distinct Field explanation must earn its necessity through prospective, information-matched tests.