pith. sign in

Explain the Lean module `IndisputableMonolith.CriminalJustice.RecidivismFromJCost`. Write a module-level guide for a reader arriving from Pith. Cover the module's purpose, the main declarations, how it fits into the Recognition Science forcing chain or runtime, and what remains outside this module. Cite only declarations present in the supplied Recognition source.

Big AI job. Grok 4.3 reads the canon and writes a Lean-grounded derivation; usually 20 seconds to 2 minutes. Your answer will appear below.
confidence: high in recognition cached

Module Guide: IndisputableMonolith.CriminalJustice.RecidivismFromJCost

Purpose

This module formalizes recidivism modeling as an instance of J-cost applied to the ratio of reoffense rate to baseline rate. Pre-intervention equilibrium occurs at ratio = 1 (zero cost). Rehabilitation that reduces the ratio below 1 increases J-cost, restoring the recognition-cost floor. The module supplies a structural certificate and identifies a one-phi-step falsifier.

Main Declarations

Fit into Recognition Science

The module applies the J-cost structure (imported from Cost) to a domain ratio, mirroring the cost-to-recognition bridge. Equilibrium at ratio 1 recovers the zero-cost self-recognition case; the phi-step supplies a concrete, falsifiable departure. It sits downstream of the cost foundation and supplies a domain-specific certificate, consistent with the structural theorems already established for J.

What Remains Outside This Module

Empirical falsification via randomized controlled trials is stated only as a named falsifier, not formalized. No integration with the full forcing-chain theorems or ledger structures is carried out inside the module.

outside recognition

Aspects Recognition does not yet address:

  • Explicit linkage to core forcing-chain declarations such as t5_holds or law_of_logic_forces_jcost
  • Runtime or ledger-integration details beyond the structural RecidivismCert

recognition modules consulted

The Recognition library is at github.com/jonwashburn/shape-of-logic. The model is restricted to the supplied Lean source and instructed not to invent theorem names. Treat output as a starting point, not a verified proof.