# Formal model of an author's textual concept system

Let an author A have a corpus

C_A = {W_1, ..., W_n}.

Each source-bound occurrence is represented as

o = (A, W, F, l, q, t, sigma, alpha, R, I, U, D, Dom, pi),

where:
- F is the work-family identifier;
- l is the locator;
- q is the source quote;
- t is the claim type;
- sigma is polarity/modality information;
- alpha is attribution status;
- pi is provenance.

A candidate author-level Reper rho is a reviewable equivalence class of authorial occurrences under a declared similarity relation ~_A:

rho = [o]_~A.

The author concept graph is

G_A = (V_A, E_A, P_A, T_A),

where V_A is the set of author-level Reper candidates, E_A typed conceptual relations, P_A provenance maps, and T_A temporal/version information.

The textual concept model is

CText_A(C_A) = (G_A, Core_A, Conflict_A, Gap_A, Evolution_A).

Core_A is not defined by one scalar truth score. Build 28 uses a vector profile:

ACP(rho) = (
  family_recurrence,
  temporal_span,
  attribution_coverage,
  D_coverage,
  Dom_coverage,
  contradiction_load,
  orphan_load
).

No universal scalarization is declared.

## Epistemic boundary

CText_A(C_A) is a model of the supplied texts and their provenance. It does not establish the author's private intentions, mental states, or the truth of the propositions represented.

`truth_layer_promotion=0`
