We develop a unified statistical-mechanical framework in which information creation—the progressive reduction of Shannon entropy through iterative correlation detection—is identified as thefundamental mechanism underlying emergent collective behavior in networks of nonlinear thresholdelements. The framework rests on three interlocking structures: (i) a quasi-static informationcreation model C(x) = [1 + e−k(x−x0)]−1 whose sigmoid form is derived from Kullback–Leiblerdivergence minimization and free-energy reduction under a prediction-error learning rule; (ii) an extended Boltzmann Transport Equation (BTE) in which the classical Stosshypothese is replaced by acorrelation-dependent scattering rate W(ω1,ω2,t) = W0(ω1,ω2)[1+ξcorr(t)], capturing the progressive buildup of correlations between nonlinear scattering events; and (iii) a mode-dependent Relaxation Time Approximation (RTAcorr) that retains spectral structure lost by the standard single-τapproximation. The extended BTE yields a self-consistent eigenvalue equation whose solutions Ωnare the collective mode frequencies (alpha, beta, gamma oscillations in the neural realization). Atthe critical threshold C(ωF) = 0.5 — the inflection point of the sigmoid and simultaneously theFermi level of the equilibrium occupation — the molecular-chaos assumption breaks down, correlatedscattering drives the distribution function away from its Fermi–Dirac baseline, and Frohlich-typecollective oscillations emerge as sharp spectral peaks in the power spectral density. We providea precise mathematical definition of emergence as this critical transition, show that it constitutesa second-order phase transition with universal critical exponents independent of the microscopicsubstrate, and demonstrate that the framework applies equally to neural populations, collective animal behavior, and any other network satisfying three minimal conditions: threshold nonlinearity ofcomponents, iterative correlation buildup, and finite effective temperature T > 0. The single mostimportant empirical prediction concerns the spectral signature at the critical developmental inflection point: a sudden appearance of spectral peaks in the EEG power spectral density, decreasedspectral entropy, and increased coherence time. We further prove that complete self-knowledgeC = 1.0 is structurally impossible—a mathematical consequence of G¨odelian self-reference, thermodynamic noise at T > 0, and measurement-theoretic self-modification—establishing epistemichumility as a necessary feature of any self-modeling system operating at finite temperature