The Four Constitutional Properties
| Property 1 Primary Purpose | CBG is AI Governance. It provides human oversight and accountability for AI-assisted work. CBG’s primary purpose is to supply the governance layer that sits on top of single-platform AI output and that makes RECCLIN dispatch and CAIPR parallel review into governed learning systems rather than AI frameworks alone. |
| Property 2 Constitutional Requirement (Unconditional Invariant) | There is no AI Governance without human authority and accountability. CBG is the mechanism that makes both structural and traceable. The checkpoint is not where the human governor is optionally present. It is where the human governor is constitutionally required to be present, documented, and accountable. This requirement is unconditional. It does not depend on prior CBG practice, domain expertise, or developmental milestones. Human authority at the checkpoint is assumed. The single constitutional boundary on that authority is the Asimov harm prohibition: no human governor may direct an AI-assisted outcome that injures a human being, allows harm through inaction, or harms humanity. That boundary does not diminish human authority. It is the ethical foundation on which that authority stands. |
| Property 3 The Injection Function | The checkpoint is an injection point for distinctly human intelligence: domain knowledge, contextual judgment, emotional response, creative intuition, and lateral synthesis that no AI platform produces alone or in combination. The checkpoint does not filter AI output. It transforms it. |
| Property 4 The Developmental Mechanism | CBG practiced through RECCLIN Reasoning output, where the AI shows its role, sources, conflicts, dissent, and confidence rather than delivering conclusions alone, produces cognitive development in the human governor. The structured output is the development mechanism. Each review cycle builds the governor’s capacity to evaluate evidence chains, recognize reasoning gaps, identify suppressed dissent, and perform the synthesis that produces augmented intelligence at the checkpoint. Property 4 is the developmental mechanism that strengthens Property 2 and makes it substantive rather than performative. It does not originate Property 2. Human authority at the checkpoint is assumed. CBG practice builds on that foundation. |
Executive Summary
There is no AI Governance without human authority and accountability. CBG is how that authority is made structural, traceable, and developmental.
CBG is AI Governance. It provides human oversight and accountability for AI-assisted work. The framework rests on four constitutional properties: CBG’s primary purpose as the governance layer for AI-assisted work; the unconditional requirement for human authority and accountability at every checkpoint; the checkpoint as an injection point for distinctly human intelligence; and the checkpoint as a developmental mechanism that builds governor capacity over time.
Responsible AI places humans in the loop. Human In The Loop means the human is present and participating, but does not require that the human holds authority or bears accountability for the outcome. AI Governance requires human authority and accountability at defined checkpoints. CBG is the mechanism that converts human presence into human authority, and participation into documented accountability.
Human authority at the checkpoint is supreme within the harm boundary. The single constitutional boundary is grounded in Isaac Asimov’s Three Laws of Robotics (1942) and the Zeroth Law (1985): no human governor may direct an AI-assisted outcome that injures a human being, allows harm through inaction, or harms humanity. That boundary is the ethical foundation on which human authority stands, not a limitation upon it.
CBG governs human oversight and accountability. How, where, and to what it is applied is variable across contexts, organizations, and risk classes. The constitutional principles remain constant. The application is variable.
Section 1 — What CBG Is and Is Not
1.1 Primary Purpose
CBG is AI Governance. It provides human oversight and accountability for AI-assisted work. CBG is distinct from ethics frameworks, which govern AI behavior, and from operational protocols, which govern AI mechanics. CBG governs the human authority layer that makes those frameworks and protocols into governed systems rather than self-validating ones.
1.2 The HITL versus AI Governance Distinction
Responsible AI places humans in the loop. Human In The Loop means the human is present and participating, but does not require that the human holds authority or bears accountability for the outcome. A human can be in the loop and still be ignored, overridden by confidence scores, or rendered ceremonial by platform convergence.
AI Governance requires human authority and accountability at defined checkpoints. CBG is the mechanism that converts human presence into human authority, and participation into documented accountability. A practitioner running RECCLIN or CAIPR with humans in the loop but without CBG is in Responsible AI mode. Adding CBG converts that practice to AI Governance mode.
1.3 What CBG Does Not Govern
CBG does not govern AI platform behavior, log formats, orchestration mechanics, or domain-specific deployment protocols. CBG does not govern performance measurement thresholds, escalation trigger specifications, or external legal liability and jurisdictional regulatory compliance. CBG does not govern measurement methodology, technical enforcement mechanisms, or platform and vendor selection. Those functions belong to their respective framework layers.
CBG governs human oversight and accountability. How that oversight is applied, where it is applied, and to what it is applied will differ across contexts, organizations, risk classes, and deployment architectures. The constitutional principles remain constant. The application is variable.
Section 2 — The Constitutional Foundation: Human Authority and Accountability
2.1 The Invariant
No AI Governance without human authority and accountability. Human authority at the checkpoint is assumed, not earned. The governance failure mode is not absence of humans. It is presence without accountability.
2.2 Property 4 as Developmental Mechanism
Property 4 is the developmental mechanism that strengthens Property 2 and makes it substantive rather than performative. It does not originate Property 2. Every human governor arrives with life experience constituting a legitimate basis for judgment within an appropriate scope. CBG practice builds on that foundation. A first-day governor carries Tier 0 authority within appropriate scope because human life experience is the qualification. Sustained CBG practice through RECCLIN Reasoning output deepens that authority over time, but does not create it.
2.3 Scope-Appropriate Checkpoint Assignment
Human authority at the checkpoint is always valid within an appropriate scope. CBG checkpoint assignments must be calibrated to the governor’s developmental stage and life experience. A governor is assumed capable of decisions within the scope that matches their experience. Placing a governor at decisions outside that scope is a governance design failure, not a human authority failure. Common sense proportionality, not credentials or age, is the standard.
2.4 The Human Governor as Tier 0
Human authority at the checkpoint is supreme within the harm boundary. In any conflict between human judgment and AI output, the human decision holds. This is not a tiebreaker rule. It is an architectural constant.
The single constitutional boundary is the harm prohibition, grounded in Isaac Asimov’s Three Laws of Robotics (1942) and the Zeroth Law (1985): a human governor cannot direct an AI-assisted outcome that injures a human being, allows harm through inaction, or harms humanity. Asimov established that even the most elegant programmed rules require a boundary that protects human safety above all other considerations. CBG applies that principle to human authority itself: the governor’s authority is supreme within that boundary, and the boundary is what makes that authority legitimate rather than arbitrary.
2.5 AI Cannot Approve AI
AI cannot satisfy, close, or validate a checkpoint for another AI. Any output that passes without completed human arbitration is not a governed decision under CBG. This is a constitutional prohibition, not an operational guideline. It is the structural enforcement of Property 2.
This prohibition applies to AI platforms. GOPEL is not an AI platform. GOPEL is a non-cognitive mechanical function that executes checkpoint logging, hash-chaining, and reporting without performing cognitive work. GOPEL records the human decision. It does not make one. Non-cognitive design removes the reasoning layer attack surface and preserves mechanical integrity when implementation controls are sound. That is a security feature, not a limitation.
No platform count, confidence score, or convergence level constitutes completed human arbitration. Source-authority discrimination begins at input classification, not at synthesis.
2.6 Convergence and Dissent Risk-Elevation Protocol
Identical convergence across platforms with absent dissent is a risk-elevation signal, not a validation signal. It requires human verification outside the AI ecosystem. Unanimity does not confirm correctness. The human governor may override full platform convergence to build on minority or absent positions. Governance authority is never subordinate to platform count or agreement percentage.
Section 3 — The Checkpoint: Three Functions
3.1 The Checkpoint Defined
A checkpoint is a formalized moment where human judgment is constitutionally required before an AI-assisted outcome is finalized. It is structural, not dispositional. The system cannot proceed past a checkpoint without the human governor exercising authority.
3.2 Function One — Governance Authority
The human governor approves, overrides, modifies, or escalates. The decision is documented with the governor’s identity, rationale, and timestamp. The record is immutable.
3.3 Function Two — Injection of Human Intelligence
The checkpoint is an injection point. The human governor brings what no AI platform contributes: domain knowledge accumulated outside the AI ecosystem, emotional response to context and consequence, creative intuition that produces lateral synthesis, and the judgment to act on a minority position against full platform convergence.
Constitutional evidence: GOPEL (the Governance Orchestrator Policy Enforcement Layer) was not suggested by any AI platform. It emerged from human creative synthesis at a CBG checkpoint. CAIPR’s brand identity emerged from a human governor recognizing that the pronunciation of the acronym sounded like the word “caper,” a term connoting skilled coordinated action, and converting that observation into a naming decision. Zero platforms produced either outcome. Both are documented instances of human creative capacity exercised at a CBG checkpoint producing results the AI ecosystem could not generate alone or in combination. Full documentation is in Section 8.
3.4 Function Three — Cognitive Development
CBG practiced through RECCLIN Reasoning output produces systematic cognitive development in the human governor. The structured output is the mechanism. Reading governed reasoning repeatedly, with role declared, sources cited, conflicts documented, dissent preserved, and confidence scored, and then evaluating, overriding, and tracking where it was right and wrong, builds the evaluation capacity that makes the checkpoint more than a procedural gate. The checkpoint develops the governor who exercises it. This is how CBG connects to HEQ measurement: what CBG builds in the human governor, HEQ measures.
3.5 The Three Functions Together
The three functions are not sequential. They operate simultaneously at every checkpoint. Function One makes the outcome accountable. Function Two makes it better than AI alone. Function Three makes the governor more capable for the next checkpoint. CBG is not overhead. CBG is the architecture of augmented intelligence.
Section 4 — The Decision Loop
4.1 Four-Stage Structure
The four-stage decision loop runs: AI Contribution, Checkpoint Evaluation, Human Arbitration, Decision Logging. This structure applies to single-platform use and to multi-AI orchestration under RECCLIN and CAIPR. The loop is constant. Complexity scales. The constitutional architecture does not change.
4.2 BEFORE / DURING / AFTER Checkpoint Architecture
Three checkpoint phases operate within the loop. BEFORE: the human governor establishes scope, criteria, and constraints. No AI execution begins without this. DURING: the governor monitors with authority to intervene, redirect, or terminate. AFTER: the governor validates output against BEFORE criteria and approves or returns for revision.
The three functions map to the three phases. BEFORE activates injection setup. DURING activates real-time injection and authority. AFTER activates governance record and development closure.
4.3 Passive Acceptance Detection
Passive acceptance at the checkpoint is a governance failure. When human arbitration becomes habitual approval without genuine review, the checkpoint loses its constitutional function. CBG requires that this failure mode be detectable. The specific detection thresholds, measurement cycles, and audit initiation timelines belong to GOPEL implementation specifications.
Section 5 — Immutability and Corrective Authority
5.1 Immutability Principle
Closed checkpoint records are immutable. Once logged, they cannot be modified.
5.2 Corrective Authority Reconciliation
Immutability and corrective authority are not in conflict. If a checkpoint record contains an error, the original record remains unaltered. Corrections are appended as new Tier 0 entries linked to the original checkpoint ID, preserving both the error and the correction in the audit trail. Immutability protects the original. Corrective authority operates by addition, not replacement.
Section 6 — Risk-Proportional Deployment
6.1 Static Checkpoint Density
Checkpoint density scales with consequence severity and is assigned at the design stage. The human governor selects a platform count and checkpoint frequency appropriate to the risk class before execution begins. Low-risk processes may operate with single checkpoints per cycle. High-consequence decisions require multiple checkpoints with independent reviewers. The constitutional architecture remains constant. Implementation complexity scales to match deployment risk.
6.2 Dynamic Escalation
When risk indicators emerge within an active session that exceed the originally assigned checkpoint density, the human governor holds constitutional authority to expand the review pool or increase checkpoint frequency in real time. Escalating when a larger conflict surfaces is not a design failure. It is CBG working as intended. The constitutional principle is that checkpoint density must always match actual consequence severity, whether that severity was anticipated at design time or emerged during execution. Specific escalation trigger conditions and risk taxonomies belong to GOPEL or domain-application briefs.
Section 7 — CBG in the HAIA Stack
7.1 Position
CBG is the enabling constitutional layer of the HAIA governance system, not a step in the adoption ladder. The adoption ladder runs: Factics, RECCLIN, CAIPR, GOPEL. CBG is the constitutional authority that makes the ladder’s outputs legitimate at every rung. A practitioner can reach any rung without CBG and remain in Responsible AI mode. Adding CBG at any rung converts the practice to AI Governance mode. CBG does not sit parallel to the ladder. It is orthogonal to it, present at every level, converting presence into authority wherever it is applied. In practical terms: CBG can be added to any AI workflow at any point, and its addition changes the governance classification of that workflow from Responsible AI to AI Governance regardless of which ladder rung the practitioner has reached.
| Component | Type | Relationship to CBG |
|---|---|---|
| RECCLIN | Role grammar | RECCLIN Reasoning output is the development mechanism for CBG Property 4. CBG makes the evaluation constitutional and developmental. |
| CAIPR | Multi-AI orchestration | CAIPR without CBG is a parallel workflow protocol. CAIPR with CBG is an AI Governance system. |
| GOPEL | Non-cognitive enforcement | CBG defines constitutional requirements. GOPEL enforces them mechanically. Non-cognitive design removes the reasoning layer attack surface. |
| HEQ / AIS | Measurement instrument | What CBG builds in the human governor, HEQ measures and expresses as the Augmented Intelligence Score (AIS). |
7.2 Relationship to RECCLIN
RECCLIN governs what each AI does and how it reports. RECCLIN Reasoning output, covering role, sources, conflicts, dissent, and confidence, is the development mechanism for CBG Property 4. RECCLIN structures the output the human governor evaluates. CBG makes the evaluation constitutional and developmental.
7.3 Relationship to CAIPR
CAIPR governs parallel multi-AI orchestration. CBG governs the human authority layer within which CAIPR operates. CAIPR without CBG is a parallel workflow protocol. CAIPR with CBG is an AI Governance system. Odd-number platform count requirements and substitution protocols are CAIPR operational requirements, not CBG constitutional requirements.
7.4 Relationship to GOPEL
GOPEL is a non-cognitive mechanical function. It automates CAIPR mechanics without performing cognitive work. GOPEL executes CBG checkpoint logging, hash-chaining, and reporting. CBG defines the constitutional requirements. GOPEL enforces them mechanically. Log format, field requirements, detection thresholds, trigger conditions, and replication guidance belong to GOPEL, not CBG. Non-cognitive design removes the reasoning layer attack surface and preserves mechanical integrity when implementation controls are sound. That is its security architecture.
7.5 Relationship to HEQ
What CBG builds in the human governor through sustained RECCLIN Reasoning output review, HEQ measures and expresses as the Augmented Intelligence Score (AIS). CBG is the practice. HEQ is the measurement instrument that evaluates whether that practice is producing genuine cognitive development and augmented intelligence.
Section 8 — Constitutional Evidence
Three documented instances from the HAIA case study record demonstrate the four constitutional properties in practice. Full session records are available at github.com/basilpuglisi/HAIA and in the published case study archive.
Evidence 1 — Function One
A human governor reviewing RECCLIN Reasoning output overrode AI consensus to build on minority dissent. Governance authority was exercised against full platform convergence. This instance demonstrates Property 2 and Function One operating as designed: the human decision held against unanimous AI output. Documented in HAIA-RECCLIN CBG Audit Log, Case Study 002 (Puglisi, 2026).
Evidence 2 — Function Two
GOPEL was named by the human governor, not suggested by any AI platform. CAIPR’s brand identity emerged from a human governor recognizing that the acronym sounded like the word “caper” and converting that observation into a naming decision. Zero platforms produced either outcome. Both are documented instances of distinctly human creative capacity exercised at a CBG checkpoint. Documented in Case Study 006, v7 (Puglisi, 2026).
Evidence 3 — Function Three and Property 4
Case Study 006 documents a cognitive development chain in which sustained RECCLIN Reasoning output practice over months developed the governor’s capacity that produced CAIPR origination. The structured output was the development mechanism. Eleven platforms described the function. Zero platforms named it. The human governor identified the name inside the convergence. This instance demonstrates Property 4 operating as a developmental mechanism rather than a performative rule. Documented in Case Study 006, v7 (Puglisi, 2026).
Section 9 — Enterprise Adoption
Organizations implement CBG progressively. The recommended starting point is the highest-risk decisions in the current workflow. Checkpoint authorities are assigned calibrated to life experience proportionality per Section 2.3. Arbitration outcomes are documented from the first session. The practice expands as institutional capacity develops.
Governance is infrastructure, not overhead. The case for CBG in enterprise contexts is not efficiency. It is accountability. Every organization deploying AI-assisted decision-making accumulates liability for those decisions. CBG makes the human authority behind each decision structural, documented, and traceable.
Section 2.3 governs how organizations operationalize common sense proportionality in checkpoint assignment decisions without requiring credential frameworks. Specific calibration to regulatory context and risk class belongs in domain-application briefs.
Section 10 — Version History
| Version | Status and Notes |
|---|---|
| v1.0 — v4.2.1 | Published versions. Constitutional foundation established. |
| v4.3 — v4.7 | Unpublished. Scope drift identified: CAIPR and GOPEL content incorporated in error. Relocation record produced. |
| v4.8 | CAIPR review input outline. Seven-platform review conducted. |
| v4.9 | Pre-publication structural outline. Five arbiter rulings incorporated. |
| v4.9 Rev 1 | Seven final arbiter rulings incorporated. Asimov attribution added. HITL distinction formalized. |
| v4.9 Rev 2 | Public-facing cleanup. All drafting markers removed. |
| v4.9 Rev 3 | Four editorial edits: supreme within harm boundary, GOPEL attack surface language, passive acceptance, review pool generalization. |
| v5.0 (this document) | Full prose publication. All constitutional properties, sections, and case study evidence in final form. |
Content Relocation Record
The following content appeared in prior CBG versions and has been formally relocated to its appropriate framework layer. No content in this table is present in CBG v5.0. All relocations are traceable to arbiter rulings.
| Content | Prior Location | Destination |
|---|---|---|
| Odd-number platform count requirement | CBG v4.7 Section 2.3 | HAIA-CAIPR Specification |
| Platform substitution protocol | CBG v4.7 Section 2.3 | HAIA-CAIPR Specification |
| Log format and field requirements | CBG v4.7 Section 3 | GOPEL |
| Replication run guidance | CBG v4.7 Section 6 | GOPEL |
| Automation bias numerical thresholds and audit rule | CBG v4.8 Section 4.3 | GOPEL |
| Escalation trigger conditions and risk taxonomies | CBG v4.8 Section 6.2 | GOPEL / Domain-application briefs |
| Seven threat domains (primary): superintelligence and existential risk, autonomous weapons, biosecurity threats, mass surveillance and privacy erosion, AI-driven fraud and disinformation, echo chambers and algorithmic polarization, corporate incentive misalignment | CBG v4.2.1 Section 10 | AI Provider Plurality Congressional Package |
| Seven threat domains (secondary publication record) | CBG v4.2.1 Section 10 | Governing AI: When Capability Exceeds Control (Puglisi, 2025) |
Conclusion
CBG is AI Governance. It provides human oversight and accountability for AI-assisted work. There is no AI Governance without human authority and accountability. That authority is supreme within the harm boundary, assumed, and grounded in the life experience every human governor brings to the checkpoint.
Isaac Asimov established in 1942 that even the most elegant programmed rules require a boundary that protects human safety above all other considerations. In 1985 he extended that boundary to humanity itself. CBG applies that principle: the governor’s authority is supreme within the harm boundary, and that boundary is the ethical foundation on which the authority stands.
The checkpoint is where three functions operate simultaneously. It makes the outcome accountable. It injects human intelligence that no AI platform produces. It develops the governor who exercises it. AI cannot approve AI. GOPEL is not AI. GOPEL is the non-cognitive infrastructure that records what the human governor decided.
CBG practiced through RECCLIN Reasoning output builds the human capacity that makes the checkpoint substantive. What CBG builds, HEQ measures and AIS expresses. Human In The Loop is not AI Governance. CBG is what makes the difference.
CBG is not overhead. It is where augmented intelligence becomes real.
References
Asimov, I. (1942). Runaround. Astounding Science Fiction.
Asimov, I. (1985). Robots and Empire. Doubleday.
Puglisi, B. C. (2025). Governing AI: When Capability Exceeds Control. basilpuglisi.com.
Puglisi, B. C. (2026). HAIA-RECCLIN Multi-AI Framework Updated for 2026. basilpuglisi.com.
Puglisi, B. C. (2026). HAIA-CAIPR: Cross AI Platform Review, Specification v1.1. basilpuglisi.com.
Puglisi, B. C. (2026). HAIA-RECCLIN CBG Audit Log, Case Study 002. basilpuglisi.com.
Puglisi, B. C. (2026). Case Study 006: The Discovery of CAIPR, v7. basilpuglisi.com.
Puglisi, B. C. (2026). GOPEL v0.6.1. github.com/basilpuglisi/HAIA.
Frequently Asked Questions
What is Checkpoint-Based Governance (CBG)?
Checkpoint-Based Governance is a constitutional framework for human-AI collaboration developed by Basil C. Puglisi, MPA. CBG provides the governance layer that sits on top of AI output and requires a named human governor to be present, documented, and accountable at defined checkpoints before any AI-assisted outcome is finalized. CBG is the mechanism that converts Human In The Loop presence into genuine human authority and participation into documented accountability.
What is the difference between Human In The Loop and AI Governance?
Human In The Loop means the human is present and participating. It does not require the human to hold authority or bear accountability for the outcome. A human can be in the loop and still be ignored, overridden by confidence scores, or rendered ceremonial by platform convergence. AI Governance requires human authority and accountability at defined checkpoints. CBG is the mechanism that makes the difference.
What are the four constitutional properties of CBG?
Property 1 (Primary Purpose): CBG is AI Governance, providing human oversight and accountability for AI-assisted work. Property 2 (Unconditional Invariant): There is no AI Governance without human authority and accountability, unconditionally. Property 3 (Injection Function): The checkpoint injects distinctly human intelligence that no AI platform produces. Property 4 (Developmental Mechanism): CBG practiced through RECCLIN Reasoning output produces cognitive development in the human governor over time.
What does “AI cannot approve AI” mean in CBG?
No AI platform can satisfy, close, or validate a checkpoint for another AI. Any output that passes without completed human arbitration is not a governed decision under CBG. No platform count, confidence score, or convergence level substitutes for human judgment at the checkpoint. This is a constitutional prohibition, not an operational preference.
What is GOPEL and why is it non-cognitive by design?
GOPEL (Governance Orchestrator Policy Enforcement Layer) is a non-cognitive mechanical function that executes CBG checkpoint logging, hash-chaining, and reporting without performing cognitive work. GOPEL is not an AI platform. It records the human decision. It does not make one. Non-cognitive design removes the reasoning layer attack surface. That is a security feature, not a limitation.
What is the Asimov harm boundary in CBG?
Human authority at the checkpoint is supreme within the harm boundary grounded in Isaac Asimov’s Three Laws of Robotics (1942) and the Zeroth Law (1985). No human governor may direct an AI-assisted outcome that injures a human being, allows harm through inaction, or harms humanity. That boundary is the ethical foundation on which human authority stands.
How does CBG fit into the HAIA stack?
CBG is the enabling constitutional layer of the HAIA governance system, not a step in the adoption ladder. The ladder runs: Factics, RECCLIN, CAIPR, GOPEL. Adding CBG at any rung converts the practice from Responsible AI mode to AI Governance mode. CBG is orthogonal to the ladder, present at every level.
What is passive acceptance detection?
Passive acceptance at the checkpoint is a governance failure. When human arbitration becomes habitual approval without genuine review, the checkpoint loses its constitutional function. CBG requires that this failure mode be detectable. Detection thresholds and timelines belong to GOPEL implementation specifications.
How does CBG handle AI platform convergence?
Identical convergence across platforms with absent dissent is a risk-elevation signal, not a validation signal. Unanimity does not confirm correctness. The human governor may override full platform convergence to build on minority or absent positions. Governance authority is never subordinate to platform count or agreement percentage.
Where can CBG v5.0 and the full HAIA framework be accessed?
CBG v5.0 and the full HAIA framework documentation are available at basilpuglisi.com and at github.com/basilpuglisi/HAIA. Related publications include Governing AI: When Capability Exceeds Control (Puglisi, 2025) and the HAIA-RECCLIN Multi-AI Framework Updated for 2026.
Acronym Key
| Acronym | Full Name |
|---|---|
| CBG | Checkpoint-Based Governance |
| HAIA | Human AI Assistant |
| RECCLIN | Researcher, Editor, Coder, Calculator, Liaison, Ideator, Navigator |
| CAIPR | Cross AI Platform Review |
| GOPEL | Governance Orchestrator Policy Enforcement Layer |
| HEQ | Human Enhancement Quotient |
| AIS | Augmented Intelligence Score |
| HITL | Human In The Loop |
| WEIRD | Western, Educated, Industrialized, Rich, Democratic |
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