Paper 65 · VII. Epistemics & Meta-Method

Iterative Constraint Construction: A Replicable Human–AI Workflow for Structured Theoretical Development

In production (complete)

A disciplined theory does not build itself. This paper explains the human–AI workflow used to construct the corpus while preventing drift, hidden assumptions, and silent revision.

Function in corpus

The methodological specification for the corpus's own construction process — upgraded from a working note (N1) to a full paper. Makes the human-AI workflow replicable by any researcher attempting similar constraint-disciplined theoretical development, and documents the governance architecture that kept the IO corpus coherent across 65 papers.

Details

Connected papers: Informational Ontology: A Structural Framework for Organizational Regimes; The Truth Protocol; Can AI Participate in Philosophical Method? A Case Study in Human–AI Co-Construction Through Informational Ontology How do you build a rigorous philosophical corpus without letting it drift? This paper formalizes the methodology used to construct Informational Ontology itself — a replicable human-AI workflow for developing constraint-disciplined theoretical systems that remain coherent across dozens of papers and hundreds of revision cycles.\n\nThe workflow is constraint-first throughout. It begins with explicit primitive declaration (what the framework assumes as given) and explicit exclusions (what it refuses to explain), then proceeds through structured iterative expansion, adversarial pressure, and version-controlled amendment discipline. Every extension must be dependency-traceable back to declared primitives. Every revision must be classified — as extension, repair, or revision — and versioned. Silent changes are not permitted.\n\nA key architectural principle: the human retains all epistemic authority. Primitive declaration, scope enforcement, drift adjudication, amendment classification, and final stabilization are human functions. AI participation is operational — variation generator, adversarial simulator, large-scale consistency amplifier — not normative. The AI does not determine what counts as a valid primitive or an acceptable explanatory cost. It helps enforce whatever the human has declared valid, at a scale and consistency no individual could maintain manually.\n\nThe paper also formalizes why theoretical incoherence characteristically arises: implicit primitive inflation, vocabulary drift, scope creep, silent revision, and patch accumulation without structural audit. The workflow is designed to surface and neutralize each of these failure modes systematically. It is domain-agnostic — applicable to philosophy, legal theory, systems design, policy modeling, or any constraint-sensitive discipline where coherence across a large body of work is required.

Availability

This paper is listed for orientation and dependency tracking. No public PDF or Zenodo record is linked yet.