Evaluation Framework

The evaluation framework is explicitly future-oriented. It does not claim empirical validation. It does not define validated instruments, final measures, scales, survey items, or metrics. It identifies candidate observable implications, possible evidence sources, feasible research designs, validity risks, reliability considerations, and rival explanations.

Every proposition is evaluable in principle. For example, identity and intent anchoring may be examined through actor agreement, intent-to-knowledge links, and representation coherence reviews. Governed knowledge admission may be studied through decision artifacts, governance reviews, and archival records. Authority qualification may be examined through AI-assisted decision sessions, review logs, and controlled human-AI studies. Governed projection may be studied through comparisons among briefings, role views, AI summaries, and source artifacts. These are candidate evaluation pathways, not findings.

Table 6 records candidate operationalizations, and Table 7 maps propositions to candidate evidence. Figure 11 presents the evaluation logic as future evidence pathways. Governance remains part of the evaluation because authority, admission, contestation, and revision are model dimensions, not external controls.

Table 6. Candidate Operationalizations

Construct	Candidate Indicator	Status
Knowledge admission	Admission records, review logs, accepted/rejected claims	Candidate only
Authority qualification	Distinction among recommendations, proposals, and decisions	Candidate only
Projection grounding	Links from projection to source knowledge and scope	Candidate only
Correspondence preservation	Reconciliation of contradictions across representations	Candidate only
Evidence-to-learning conversion	Governed disposition of outcomes into revised knowledge	Candidate only

Table 7. Proposition-to-Evidence Mapping

Proposition Family	Candidate Evidence	Rival Explanations
Identity and intent	Actor agreement, intent-to-knowledge links	Culture, hierarchy
Admission and authority	Decision records, governance reviews	Managerial expertise, training
Projection and correspondence	Cross-role view comparison, source fidelity checks	Interface quality, boundary objects
Execution and learning	Decision-work-evidence links, retrospectives	Project controls, professional expertise
Human-AI contribution	Source-grounded AI outputs, escalation logs	AI model quality, user expertise

Evaluation Logic and Future Evidence Pathways. The figure presents a left-to-right conceptual sequence of identity and intent, governed knowledge, representation and projection, decision and execution, and evidence and learning. A governance layer connects authority, provenance, freshness, confidence, and contestability across the sequence. Dashed feedback indicates learning and revision over time. Color is not required for interpretation. — Figure 11. Evaluation Logic and Future Evidence Pathways. Shows candidate evidence pathways and research designs as future evaluation logic, not established empirical findings. The figure is conceptual and does not specify software architecture, workflow automation, user-interface behavior, or empirical findings.

A plausible future study would use a longitudinal comparative case design in knowledge-intensive organizations adopting AI-mediated decision support. Researchers would compare how source grounding, authority qualification, memory-state distinction, and projection governance affect interpretation coherence and decision traceability over time while testing rival explanations such as hierarchy, culture, technical integration, professional expertise, and AI model capability. Such a design would evaluate OKK mechanisms without treating software integration as proof of organizational knowledge continuity.