proofappendix

proof:appC_phi_minimized_entropy_per_complexity

proof:appC_phi_minimized_entropy_per_complexity

Exact LaTeX body

\begin{proof}
\label{proof:appC_phi_minimized_entropy_per_complexity}
By Theorem~\ref{theorem:appC_phi_min_growth}, every sustainable $\lambda$ satisfies
$\lambda\ge\varphi>1$. On $(1,\infty)$,
\[
\mathcal{I}'(\lambda)=1-\frac{1}{\lambda^2}>0,
\]
so $\mathcal{I}$ is strictly increasing throughout the feasible interval
$[\varphi,\infty)$. Therefore the minimum over sustainable rates occurs at the
left endpoint $\lambda=\varphi$.
\end{proof}

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definitiondefinitionalappendix

$\varphi$-Stable Region

definition:appC_phi_stable_region

Exact LaTeX body

\begin{definition}[$\varphi$-Stable Region]
\label{definition:appC_phi_stable_region}
Let $\Phi$ be the entropy-minimizing reflective update map on the observer's
symbolic manifold. A region $M_\varphi$ is \emph{$\varphi$-stable} if:
\begin{enumerate}
    \item it is invariant under the update, $\Phi(M_\varphi)\subseteq M_\varphi$;
    \item along $M_\varphi$ the curvature operator satisfies
    \[
    \langle K_t(v), v \rangle = \varphi^{-1} \|v\|^2;
    \]
    \item there is a neighborhood $U$ of $M_\varphi$ and a constant $q<1$ such that
    \[
    d(\Phi(x),M_\varphi)\le q\,d(x,M_\varphi)\qquad(x\in U).
    \]
\end{enumerate}
\end{definition}
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lemmaprovenappendix

Geodesic Convergence to $M_\varphi$

lemma:appC_geodesic_convergence

Exact LaTeX body

\begin{lemma}[Geodesic Convergence to $M_\varphi$]
\label{lemma:appC_geodesic_convergence}
If an observer trajectory $x_{n+1}=\Phi(x_n)$ remains in the neighborhood $U$ of a
$\varphi$-stable region $M_\varphi$, then $x_n$ converges to $M_\varphi$ in
observer-relative distance:
\[
 d(x_n,M_\varphi)\le q^n d(x_0,M_\varphi)\longrightarrow0 .
\]
\end{lemma}
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proofappendix

proof:appC_geodesic_convergence

proof:appC_geodesic_convergence

Exact LaTeX body

\begin{proof}
\label{proof:appC_geodesic_convergence}
The contraction clause in Def.~\ref{definition:appC_phi_stable_region} gives
$d(x_{n+1},M_\varphi)=d(\Phi(x_n),M_\varphi)\le qd(x_n,M_\varphi)$ whenever
$x_n\in U$. Iterating yields
$d(x_n,M_\varphi)\le q^n d(x_0,M_\varphi)$. Since $0\le q<1$, $q^n\to0$, so the
distance from the trajectory to $M_\varphi$ tends to zero. Invariance of
$M_\varphi$ ensures that once the trajectory reaches the stable region it remains
there.
\end{proof}

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scholiumappendix

Symbolic–Geometric Equivalence of $\varphi$

scholium:appC_symbolic_geometric_equivalence

Exact LaTeX body

\begin{scholium}[Symbolic–Geometric Equivalence of $\varphi$]
\label{scholium:appC_symbolic_geometric_equivalence}
The golden ratio appears in symbolic thermodynamics, curvature operators, and recursive observer models. It is a structural attractor unifying symbolic emergence (cf. Scholium~\ref{scholium:appC_two_horizons_co_constitutive}) and the memory-based geometry of time (Scholium~\ref{scholium:appC_time_as_memory}).
\end{scholium}

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definitiondefinitionalappendix

Symbolic Operator Assumptions

definition:appC_symbolic_operator_assumptions

Exact LaTeX body

\begin{definition}[Symbolic Operator Assumptions]
\label{definition:appC_symbolic_operator_assumptions}
Assume symbolic emergence is represented by a positive two-step complexity
sequence $(s_n)$ whose state vector is
\[
\mathbf{s}_n=(s_n,s_{n-1})^T.
\]
The minimal drift--reflection closure preserves current symbolic content and one
memory trace:
\[
s_{n+1}=s_n+s_{n-1}.
\]
Thus Drift contributes the current term $s_n$, Reflection contributes the retained
memory term $s_{n-1}$, and recursive emergence is their balanced composition.
\end{definition}
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lemmaprovenappendix

Matrix Representation of Symbolic Operators

lemma:appC_matrix_representation_symbolic_operators

Exact LaTeX body

\begin{lemma}[Matrix Representation of Symbolic Operators]
\label{lemma:appC_matrix_representation_symbolic_operators}
Under the two-step closure of
Def.~\ref{definition:appC_symbolic_operator_assumptions}, symbolic evolution is
represented by
\[
M=\begin{pmatrix}1&1\\1&0\end{pmatrix},
\qquad
\mathbf{s}_{n+1}=M\mathbf{s}_n.
\]
\end{lemma}

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proofappendix

proof:appC_matrix_rep_symbolic_operators

proof:appC_matrix_rep_symbolic_operators

Exact LaTeX body

\begin{proof}
\label{proof:appC_matrix_rep_symbolic_operators}
By definition,
$s_{n+1}=s_n+s_{n-1}$ and the memory coordinate updates by
$s_n\mapsto s_n$. Therefore
\[
\begin{pmatrix}s_{n+1}\\s_n\end{pmatrix}
=\begin{pmatrix}s_n+s_{n-1}\\s_n\end{pmatrix}
=\begin{pmatrix}1&1\\1&0\end{pmatrix}
\begin{pmatrix}s_n\\s_{n-1}\end{pmatrix}.
\]
This proves the claimed matrix representation.
\end{proof}
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theoremprovenappendix

Golden Ratio as Eigenvalue of Recursive Emergence

theorem:appC_phi_eigenvalue_recursive_emergence

Exact LaTeX body

\begin{theorem}[Golden Ratio as Eigenvalue of Recursive Emergence]
\label{theorem:appC_phi_eigenvalue_recursive_emergence}
The golden ratio $\varphi$ is the Perron--Frobenius eigenvalue, hence the dominant
asymptotic growth factor, of the minimal recursive-emergence matrix
$M=\bigl(\begin{smallmatrix}1&1\\1&0\end{smallmatrix}\bigr)$.
\end{theorem}
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  "latex_body": "\\begin{theorem}[Golden Ratio as Eigenvalue of Recursive Emergence]\n\\label{theorem:appC_phi_eigenvalue_recursive_emergence}\nThe golden ratio $\\varphi$ is the Perron--Frobenius eigenvalue, hence the dominant\nasymptotic growth factor, of the minimal recursive-emergence matrix\n$M=\\bigl(\\begin{smallmatrix}1&1\\\\1&0\\end{smallmatrix}\\bigr)$.\n\\end{theorem}",
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proofappendix

proof:appC_phi_eigenvalue_recursive_emergence

proof:appC_phi_eigenvalue_recursive_emergence

Exact LaTeX body

\begin{proof}
\label{proof:appC_phi_eigenvalue_recursive_emergence}
The characteristic polynomial is
\[
\det(M-\mu I)=\det\begin{pmatrix}1-\mu&1\\1&-\mu\end{pmatrix}
=\mu^2-\mu-1.
\]
Its roots are
\[
\mu_+=\frac{1+\sqrt5}{2}=\varphi,
\qquad
\mu_- =\frac{1-\sqrt5}{2}=-\varphi^{-1}.
\]
Since $|\mu_-|<\mu_+$, the spectral radius is $\varphi$. The matrix has strictly
positive powers after finitely many steps, so the Perron--Frobenius eigenvalue is
real, positive, simple, and equal to this spectral radius. Hence generic positive
state vectors grow asymptotically at rate $\varphi$.
\end{proof}
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  "latex_body": "\\begin{proof}\n\\label{proof:appC_phi_eigenvalue_recursive_emergence}\nThe characteristic polynomial is\n\\[\n\\det(M-\\mu I)=\\det\\begin{pmatrix}1-\\mu&1\\\\1&-\\mu\\end{pmatrix}\n=\\mu^2-\\mu-1.\n\\]\nIts roots are\n\\[\n\\mu_+=\\frac{1+\\sqrt5}{2}=\\varphi,\n\\qquad\n\\mu_- =\\frac{1-\\sqrt5}{2}=-\\varphi^{-1}.\n\\]\nSince $|\\mu_-|<\\mu_+$, the spectral radius is $\\varphi$. The matrix has strictly\npositive powers after finitely many steps, so the Perron--Frobenius eigenvalue is\nreal, positive, simple, and equal to this spectral radius. Hence generic positive\nstate vectors grow asymptotically at rate $\\varphi$.\n\\end{proof}",
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lemmaprovenappendix

Fibonacci Structure via Matrix Powers

lemma:appC_fibonacci_structure_matrix_powers

Exact LaTeX body

\begin{lemma}[Fibonacci Structure via Matrix Powers]
\label{lemma:appC_fibonacci_structure_matrix_powers}
Let $F_0=0$, $F_1=1$, and $F_{n+1}=F_n+F_{n-1}$. For
$M=\bigl(\begin{smallmatrix}1&1\\1&0\end{smallmatrix}\bigr)$,
\[
M^n=\begin{pmatrix}F_{n+1}&F_n\\F_n&F_{n-1}\end{pmatrix}\qquad(n\ge1).
\]
\end{lemma}
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proofappendix

proof:appC_fibonacci_matrix_powers

proof:appC_fibonacci_matrix_powers

Exact LaTeX body

\begin{proof}
\label{proof:appC_fibonacci_matrix_powers}
For $n=1$ the formula gives
$\bigl(\begin{smallmatrix}F_2&F_1\\F_1&F_0\end{smallmatrix}\bigr)
=\bigl(\begin{smallmatrix}1&1\\1&0\end{smallmatrix}\bigr)=M$. Assume the formula
holds for $n$. Then
\[
M^{n+1}=M^nM
=\begin{pmatrix}F_{n+1}&F_n\\F_n&F_{n-1}\end{pmatrix}
 \begin{pmatrix}1&1\\1&0\end{pmatrix}
=\begin{pmatrix}F_{n+1}+F_n&F_{n+1}\\F_n+F_{n-1}&F_n\end{pmatrix}
=\begin{pmatrix}F_{n+2}&F_{n+1}\\F_{n+1}&F_n\end{pmatrix}.
\]
This is the formula with $n$ replaced by $n+1$.
\end{proof}
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propositionprovenappendix

Conditional Minimality of 2×2 Form

proposition:appC_conditional_minimality_2x2

Exact LaTeX body

\begin{proposition}[Conditional Minimality of 2×2 Form]
\label{proposition:appC_conditional_minimality_2x2}
Under the assumption that symbolic emergence requires encoding both current state
and one memory state, the 2×2 matrix form is minimal for representing the
drift-reflection composition (see Axiom~\ref{axiom:appC_axiom_of_memory}).
\end{proposition}

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proofappendix

proof:appC_conditional_minimality_2x2

proof:appC_conditional_minimality_2x2

Exact LaTeX body

\begin{proof}
\label{proof:appC_conditional_minimality_2x2}
A one-dimensional linear state stores only one scalar degree of freedom at step
$n$. It can represent a Markov update $s_{n+1}=a s_n$, but it cannot distinguish
two histories with the same current value $s_n$ and different previous values
$s_{n-1}$, even though the required recursion
$s_{n+1}=f(s_n,s_{n-1})$ depends on both. Therefore dimension one is insufficient
for memory-dependent emergence. Dimension two is sufficient, because the state
vector $(s_n,s_{n-1})^T$ and the matrix in
Lemma~\ref{lemma:appC_matrix_representation_symbolic_operators} exactly encode the
current value and one retained memory trace. Hence $2\times2$ is minimal under the
stated one-step-memory assumption.
\end{proof}

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definitiondefinitionalappendix

Bounded Symbolic Observer Dynamics

definition:appC_bounded_symbolic_observer_dynamics

Exact LaTeX body

\begin{definition}[Bounded Symbolic Observer Dynamics]
\label{definition:appC_bounded_symbolic_observer_dynamics}
Consider a symbolic observer with bounded attention radius $\delta$ (cf.~\ref{definition:bk4_bounded_observer}) navigating meaning space. The observer experiences:
\begin{itemize}
\item Forward drift: tendency to explore new symbolic territory at rate $\theta$
\item Reflective curvature: memory-based constraint pulling back with strength $1/\theta$
\item Bounded exploration: total symbolic displacement must remain finite
\end{itemize}
\end{definition}

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definitiondefinitionalappendix

Symbolic Curvature Function

definition:appC_symbolic_curvature_function

Exact LaTeX body

\begin{definition}[Symbolic Curvature Function]
\label{definition:appC_symbolic_curvature_function}
The total symbolic curvature experienced by the observer (Def.~\ref{definition:appC_bounded_symbolic_observer_dynamics}) is:
\[
\kappa(\theta) = \theta + \frac{1}{\theta}
\]
where $\theta > 0$ represents the ratio of forward drift to reflective strength.
\end{definition}

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lemmaprovenappendix

Geometric Interpretation of Curvature Terms

lemma:appC_geometric_interpretation_curvature

Exact LaTeX body

\begin{lemma}[Geometric Interpretation of Curvature Terms]
\label{lemma:appC_geometric_interpretation_curvature}
The term $\theta$ represents symbolic drift velocity, while $1/\theta$ represents
the curvature penalty imposed by bounded memory. The sum $\kappa(\theta)$ measures
total symbolic effort required to maintain coherent exploration (cf.
Def.~\ref{definition:bk6_symbolic_curvature_tensor}).
\end{lemma}

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proofappendix

proof:appC_geometric_interpretation_curvature

proof:appC_geometric_interpretation_curvature

Exact LaTeX body

\begin{proof}
\label{proof:appC_geometric_interpretation_curvature}
By Def.~\ref{definition:appC_bounded_symbolic_observer_dynamics}, $\theta$ is the
forward exploration rate, so its contribution to one-step effort is linear in
$\theta$ after normalization of units. The reflective term must decrease as drift
increases and increase as drift slows, because slower forward motion forces a
larger fraction of the step to be spent maintaining memory coherence. The
scale-free reciprocal $1/\theta$ is the unique reciprocal penalty normalized to
be $1$ at the balanced point $\theta=1$. Since the two costs are paid in the same
step and in the same normalized units, finite symbolic effort is their additive
sum $\kappa(\theta)=\theta+1/\theta$.
\end{proof}

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theoremprovenappendix

Golden Ratio as Minimal Curvature Parameter

theorem:appC_phi_minimal_curvature_parameter

Exact LaTeX body

\begin{theorem}[Golden Ratio as Minimal Curvature Parameter]
\label{theorem:appC_phi_minimal_curvature_parameter}
The parameter $\theta = \varphi$ minimizes the symbolic curvature function
$\kappa(\theta)$ among nondegenerate recursively sustainable exploration
parameters, i.e. among $\theta$ satisfying $\theta\ge 1+1/\theta$.
\end{theorem}
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proofappendix

proof:appC_phi_minimal_curvature

proof:appC_phi_minimal_curvature

Exact LaTeX body

\begin{proof}
\label{proof:appC_phi_minimal_curvature}
The recursive sustainability constraint is
\[
\theta\ge 1+\frac{1}{\theta},
\]
which is equivalent, for $\theta>0$, to
$\theta^2-\theta-1\ge0$. Hence the feasible set is
$[\varphi,\infty)$, where $\varphi=(1+\sqrt5)/2$. On this interval,
\[
\kappa'(\theta)=1-\frac{1}{\theta^2}>0,
\]
because $\theta\ge\varphi>1$. Thus $\kappa$ is strictly increasing on the feasible
set and its minimum occurs at the left endpoint $\theta=\varphi$. The minimized
curvature is
\[
\kappa(\varphi)=\varphi+\frac1\varphi=\varphi+(\varphi-1)=2\varphi-1=\sqrt5.
\]
The unconstrained point $\theta=1$ is lower for $\kappa$ alone, but it violates
the nondegenerate recursive sustainability constraint $\theta\ge1+1/\theta$ and
therefore represents stagnation rather than sustained symbolic exploration.
\end{proof}
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definitiondefinitionalappendix

Symbolic Flow Stability

definition:appC_symbolic_flow_stability

Exact LaTeX body

\begin{definition}[Symbolic Flow Stability]
\label{definition:appC_symbolic_flow_stability}
A symbolic flow is stable if small perturbations in the exploration parameter $\theta$ decay exponentially. The stability condition requires:
\[
\left| \frac{d}{d\theta} \left( 1 + \frac{1}{\theta} \right) \right|_{\theta=\varphi} < 1
\]
(cf. Def.~\ref{definition:bk6_reflection_operator_complete})
\end{definition}

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lemmaprovenappendix

Stability of $\varphi$-Flow

lemma:appC_stability_phi_flow

Exact LaTeX body

\begin{lemma}[Stability of $\varphi$-Flow]
\label{lemma:appC_stability_phi_flow}
The $\varphi$-flow satisfies the stability condition (cf.~\ref{definition:appC_symbolic_flow_stability}).
\end{lemma}

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proofappendix

proof:appC_stability_phi_flow

proof:appC_stability_phi_flow

Exact LaTeX body

\begin{proof}
\label{proof:appC_stability_phi_flow}
Let $f(\theta)=1+1/\theta$. Then $f'(\theta)=-1/\theta^2$, so
\[
|f'(\varphi)|=\frac{1}{\varphi^2}=2-\varphi<1.
\]
By the one-dimensional fixed-point stability criterion, sufficiently small
perturbations of the iteration $\theta_{n+1}=f(\theta_n)$ contract in a
neighborhood of $\varphi$. Hence the $\varphi$-flow satisfies the stated stability
condition.
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theoremprovenappendix

Unified Recursive Fixed Point

theorem:appC_unified_recursive_fixed_point

Exact LaTeX body

\begin{theorem}[Unified Recursive Fixed Point]
\label{theorem:appC_unified_recursive_fixed_point}
Both the matrix eigenvalue approach (cf.~\ref{lemma:appC_matrix_representation_symbolic_operators}) and the topological curvature approach converge to the same fixed-point equation:
\[
\lambda = 1 + \frac{1}{\lambda} \Rightarrow \lambda^2 - \lambda - 1 = 0 \Rightarrow \lambda = \varphi
\]
for the unique positive nondegenerate fixed point.
\end{theorem}

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proofappendix

proof:appC_unified_recursive_fixed_point

proof:appC_unified_recursive_fixed_point

Exact LaTeX body

\begin{proof}
\label{proof:appC_unified_recursive_fixed_point}
In the matrix approach, the minimal memory-preserving recurrence is
$C_{n+1}=C_n+C_{n-1}$. If the positive asymptotic ratio
$\lambda=\lim C_{n+1}/C_n$ exists, division by $C_n$ and passage to the limit give
\[
\lambda=1+\frac{1}{\lambda}.
\]
In the topological approach, recursive sustainable exploration requires that the
forward parameter equal one unit of new exploration plus the reciprocal
reflective correction, so its fixed point satisfies the same equation
$\theta=1+1/\theta$. In both cases the positive solution of
$x^2-x-1=0$ is $x=\varphi$, while the other solution is negative and therefore
inadmissible as a growth or curvature parameter.
\end{proof}
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scholiumappendix

Structural Universality of $\varphi$

scholium:appC_structural_universality_phi

Exact LaTeX body

\begin{scholium}[Structural Universality of $\varphi$]
\label{scholium:appC_structural_universality_phi}
The independent emergence of $\varphi$ from matrix spectral theory and topological curvature analysis suggests that $\varphi$ represents a fundamental structural constant of bounded recursive systems. This convergence transcends particular mathematical representations, indicating an intrinsic property of symbolic emergence under resource constraints (see Def.~\ref{definition:bk6_symbolic_curvature_tensor}, Thm.~\ref{theorem:bk6_symbolic_diffusion_governs_evolution}).
\end{scholium}

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remarkappendix

Connection to Other Symbolic Modalities

remark:appC_connection_other_modalities

Exact LaTeX body

\begin{remark}[Connection to Other Symbolic Modalities]
\label{remark:appC_connection_other_modalities}
The fixed-point equation $\lambda = 1 + 1/\lambda$ (cf.~\ref{theorem:appC_unified_recursive_fixed_point}) appears in multiple contexts within symbolic dynamics. The consistent emergence of $\varphi$ across matrix, topological, and (potentially) thermodynamic or spectral approaches is not a coincidence to be noted but a transfer to be proved: the Modal Transference Theorem below states the conditions under which an ordinal-recursive invariant such as $\varphi$ is carried, intact, from one observer-accessible carrier to another.
\end{remark}

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sectionsectionappendix

Modal Transference of Symbolic Invariants

sec:appC_modal_transference

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definitiondefinitionalappendix

Symbolic modality

definition:appC_symbolic_modality

Exact LaTeX body

\begin{definition}[Symbolic modality]
\label{definition:appC_symbolic_modality}
A \emph{symbolic modality} is a tuple
\[
\mathfrak{M} = (X_\mathfrak{M},\ \preceq_\mathfrak{M},\ d_{\Obs,\mathfrak{M}},\
E_\mathfrak{M},\ \mathcal{I}_\mathfrak{M}),
\]
where $X_\mathfrak{M}$ is a space of modal presentations, $\preceq_\mathfrak{M}$
is an observer-resolved emergence order, $d_{\Obs,\mathfrak{M}}$ is the
observer-relative modal distance, $E_\mathfrak{M}$ is the stage-composite
emergence operator (cf.~Def.~\ref{definition:bk1_stage_composite_operator}) in
the modality, and $\mathcal{I}_\mathfrak{M}$ is a family of structural invariants
(cyclic order, adjacency, recurrence spectrum, proportion, curvature signature).
\end{definition}

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definitiondefinitionalappendix

Modal transference map

definition:appC_modal_transference_map

Exact LaTeX body

\begin{definition}[Modal transference map]
\label{definition:appC_modal_transference_map}
Let $\mathfrak{M}_A,\mathfrak{M}_B$ be symbolic modalities. A \emph{modal
transference map} is an observer-bounded map
$T_{A\to B}:X_{\mathfrak{M}_A}\to X_{\mathfrak{M}_B}$ satisfying:
\begin{enumerate}
\item \emph{Ordinal preservation:}
$x\preceq_{\mathfrak{M}_A}y \Rightarrow T_{A\to B}(x)\preceq_{\mathfrak{M}_B}T_{A\to B}(y)$.
\item \emph{Observer-bounded distortion:} there exist $L<\infty$ and
$\varepsilon_\Obs\ge 0$ with
\[
d_{\Obs,\mathfrak{M}_B}\!\big(T_{A\to B}x,\,T_{A\to B}y\big)
\le L\, d_{\Obs,\mathfrak{M}_A}(x,y) + \varepsilon_\Obs.
\]
\item \emph{Operator semi-conjugacy:}
$T_{A\to B}\circ E_{\mathfrak{M}_A} \sim_\Obs E_{\mathfrak{M}_B}\circ T_{A\to B}$,
equality holding up to observer resolution $\varepsilon_\Obs$.
\item \emph{Invariant preservation:} for each $I\in\mathcal{I}_{\mathfrak{M}_A}$
transferred by $T_{A\to B}$ there is $T_*I\in\mathcal{I}_{\mathfrak{M}_B}$ with
$I(x)=T_*I(T_{A\to B}x)$ up to $\varepsilon_\Obs$.
\end{enumerate}
\end{definition}
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theoremprovenappendix

Modal Transference

theorem:appC_modal_transference

Exact LaTeX body

\begin{theorem}[Modal Transference]
\label{theorem:appC_modal_transference}
Let $T_{A\to B}$ be a modal transference map between symbolic modalities
$\mathfrak{M}_A$ and $\mathfrak{M}_B$. Then any invariant determined only by
ordinal order, operator recurrence, cyclic adjacency, or spectral proportion is
preserved across the transfer up to observer resolution. In particular, if a
recurrence invariant $\lambda$ is fixed in $\mathfrak{M}_A$ by the balanced
two-step closure $a_{n+1}=a_n+a_{n-1}$
(cf.~book V, Def.~\ref{definition:bk5_balanced_two_step_memory_closure}), then its
transferred presentation in $\mathfrak{M}_B$ carries the same positive spectral
invariant $\lambda=\varphi$.
\end{theorem}

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proofappendix

proof:appC_modal_transference

proof:appC_modal_transference

Exact LaTeX body

\begin{proof}
\label{proof:appC_modal_transference}
By ordinal preservation, $T_{A\to B}$ preserves the emergence order of the source
modality; by observer-bounded distortion, differences below the observer
threshold in $\mathfrak{M}_A$ remain below threshold in $\mathfrak{M}_B$. By
operator semi-conjugacy the transferred system follows the same emergence
dynamics up to resolution: $T_{A\to B}\circ E_{\mathfrak{M}_A} \sim_\Obs
E_{\mathfrak{M}_B}\circ T_{A\to B}$. An invariant fixed solely by the recurrence
or adjacency structure of $E_{\mathfrak{M}_A}$ cannot change when $E_{\mathfrak{M}_A}$
is replaced by its semi-conjugate presentation $E_{\mathfrak{M}_B}$ except below
threshold. For the balanced two-step closure the companion matrix is
$A=\big(\begin{smallmatrix}1&1\\1&0\end{smallmatrix}\big)$, with characteristic
equation $\lambda^2-\lambda-1=0$ and positive root $\varphi$. Since transference
preserves the recurrence structure, the same spectral invariant appears in the
target modality. Hence $\varphi$ is not tied to a sensory carrier; it is an
ordinal-recursive invariant transferred through modal presentation.
\end{proof}
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scholiumappendix

Sonification and the Chromatic Wheel as Transference Tests

scholium:appC_transference_tests

Exact LaTeX body

\begin{scholium}[Sonification and the Chromatic Wheel as Transference Tests]
\label{scholium:appC_transference_tests}
The sonification and chromatic-wheel constructions are not offered as analogies.
They are modal transference tests. Each asks whether an invariant first defined
in ordinal-symbolic form survives transfer into a distinct observer-accessible
carrier. Sonification is the transference map
$T_{\mathrm{symbolic}\to\mathrm{audio}}$ carrying symbolic order into pitch,
interval, rhythm, and phase; the Newtonian color wheel is the map
$T_{\mathrm{symbolic}\to\mathrm{chromatic}}$ carrying cyclic adjacency,
opposition, and return into visual structure. When cyclic order, recurrence
spectrum, and bounded adjacency are preserved under
Def.~\ref{definition:appC_modal_transference_map}, the invariant belongs to the
symbolic structure rather than to the particular sensory modality. This is the
precise sense in which domains are modal presentations of shared ordinal-symbolic
invariants---not the claim that everything is the same substance, but the claim
that distinct modalities preserve the same emergence grammar when the
transference map respects ordinal order, observer bounds, and operator recurrence.
\end{scholium}

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