Learning Transition Governance

Status

Canonical source repository: Admissible-Existence/learning-transition-governance

Wiki state: watching

Detail status: initial public-safe canonical detail page

Definition

Learning Transition Governance is a canonical formalism for evaluating how learning, adaptation, model updates, feedback intake, or knowledge-state changes affect transition standing.

It supports inspection of whether a system can change what it knows or how it acts without silently losing governance, boundary, continuity, or admissibility standing.

Source Boundary

Admissible-Existence defines the formalism. Publisher publishes papers and public exposition. The Admissibility Wiki mirrors, relates, crosswalks, and discovers relationships.

The wiki is not the source authority for this formalism.

Scope

Learning Transition Governance applies where an adaptive process, model update, policy refinement, feedback cycle, or knowledge-state transition could change later authority, evidence, boundary, or execution standing.

Purpose

The purpose of Learning Transition Governance is to prevent learning or adaptation from becoming an ungoverned transition path that bypasses review, receipts, boundaries, or commit-time admissibility checks.

Core Constructs

Construct	Role
Learning transition	Change in learned state, model state, rule state, or knowledge posture.
Learning boundary	Constraint around what may be learned, retained, applied, or propagated.
Feedback evidence	Input that may affect a learned state.
Adaptation standing	Whether a change may be used by later actions.
Rebinding	Renewed check against current authority, policy, evidence, and boundary state.
Receipt	Record supporting reconstruction of the learning transition path.

Related Canonical Formalisms

Related Formalism	Relationship
Core-Lite Admissibility Engine	May gate learning transitions and emit receipts.
Runtime Transition Governance	Applies checks when learned state affects execution or publication.
Transition Table	Classifies learning-bearing transition posture.
Boundary Conditions	Defines learning and application boundaries.
State Transition Continuity Model	Evaluates continuity across learned-state changes.
Decision Continuity	Preserves decision standing across learning updates.
Data Continuity	Preserves evidence and data standing across feedback intake.
Triad Governance Model	Supports role-separated governance over learning changes.

Mathematical Candidates

Status: pending source-confirmed extraction from canonical source or publication artifacts.

Proof Candidates

Status: pending source-confirmed extraction from canonical source or publication artifacts.

Validation Candidates

Status: pending source-confirmed extraction from canonical source or publication artifacts.

Validation candidates should include unapproved learning, stale feedback, model-state drift, policy-changing feedback, and learning-boundary failure cases when public-safe source artifacts are available.

Publication Artifacts

Status: pending source-confirmed extraction.

Reference Implementations

Status: pending source-confirmed extraction.

External Crosswalk Targets

External Framework	Relationship
GLM	May declare learning-scope claims, non-claims, and composition boundaries before adaptation.
EVIDE	May preserve post-event evidence needed to reconstruct learning transition standing.

These are crosswalk targets only. They do not replace canonical formalism definition.

Open Questions

Which exact source file defines Learning Transition Governance?
Which learning states are canonical versus explanatory?
Which tests validate model-state drift, stale feedback, and learning-boundary failure?
Which receipt fields are required for reconstructing learning transition standing?

Non-Claims

This wiki page does not define, prove, validate, or implement the formalism. External framework mappings are crosswalk candidates, not equivalence decisions. A listed relationship does not imply accepted formal equivalence.