J-Editorial Framework

A comprehensive three-layer editorial framework for knowledge management, documentation, and content publishing.

Overview¶

J-Editorial provides a sophisticated metadata and workflow system that separates:

• What artifacts ARE (intrinsic properties)

• What artifacts MEAN (extrinsic dimensions)

• How artifacts BEHAVE (operational rules)

This separation enables flexible, context-aware workflows across diverse use cases—from personal knowledge management to team documentation to regulatory compliance.

Traditional content management systems conflate what content is with what it means and how it should behave. This creates rigid, inflexible systems that struggle to adapt to different contexts and workflows.

J-Editorial solves this by cleanly separating three distinct concerns.

Three-Layer Architecture¶

Layer 1: Properties¶

Properties that describe what an artifact fundamentally is, independent of context or interpretation.

---
refinement: 0.75          # Quality score (0.00-1.00)
origin: requirement       # Why created
form: stable              # Permanence intent
audience: internal        # Intended visibility
stubs: ["Gap 1"]         # Acknowledged gaps
---

These properties:

• Are stored directly in document frontmatter

• Remain stable across different contexts

• Are portable across tools and platforms

• Describe inherent characteristics

Layer 2: Dimensions¶

20 dimensions that interpret what artifacts mean in specific contexts:

State Dimensions (6):

• health - Gate-agnostic overall quality

• usefulness - Gate-aware fitness for audience

• compliance_fit - Policy and license alignment

• trust_level - Validation status and evidence quality

• freshness - Recency normalized to form-appropriate cadences

• coverage_fit - Rubric/schema conformance

Trajectory Dimensions (7):

• drift - Alignment decay over time (decomposed)

• health_trend - Quality change direction and velocity

• adoption - Engagement and usage trend

• potential_energy - Latent value in unresolved vectors

• friction_coefficient - Proportion of blocked vectors

• editorial_velocity - Rate of vector resolution

• refinement_velocity - Rate of quality improvement

Network Dimensions (2):

• network_position - Structural importance in knowledge graph

• propagation_risk - Cascade potential and blast radius

Workflow Dimensions (2):

• stage - Position in defined workflow (transition-based)

• blocking_status - Impediments preventing progress

Priority Dimensions (3):

• attention_priority - Urgency and importance synthesis

• retention_value - Keep vs archive guidance

• effort_to_improve - Estimated work to reach target

These dimensions are calculated in dashboards, not stored in frontmatter.

Layer 3: Rules¶

Automated behaviors grounded in philosophical frameworks:

• Automation: Auto-promote artifacts through workflow stages

• Enforcement: Block actions that violate policies

• Governance: Enforce role-based approvals and consent

• Notification: Alert stakeholders of critical states

• Compliance: Validate regulatory requirements

• Audit: Track all decisions with rationale

Layer 3 is grounded in a philosophical framework with three anthropological axes:

• Circular (human↔human): Collaboration, consent, governance

• Radial (human↔world): Tools, automation, infrastructure

• Angular (human↔ideational): Standards, policies, norms

Use Cases¶

Personal Knowledge Management (PKM)¶

Build interconnected knowledge graphs that evolve from fleeting notes to evergreen content.

Team Documentation¶

Collaborative authoring with quality gates, review workflows, and automated compliance checks.

Content Publishing¶

Editorial calendar management tracking articles from ideation through publication.

Project Management¶

Lifecycle tracking with dependency management and archive/retention decisions.

Compliance & Governance¶

Role-based access control, policy enforcement, and audit trails for regulated environments.

Key Benefits¶

Current Status¶

Version: 1.0.0 (Released 2025-11-29)

Implementation:

• Reference implementation in Obsidian with Dataview

• Rust parser under development

• Full documentation with working examples

Availability:

• Framework documentation and specifications available

• Public release of full implementation: Coming Soon

Design Philosophy¶

J-Editorial is built on six core principles:

1. Separation of Concerns: Properties, dimensions, and behaviors are distinct

2. Context Independence: Metadata shouldn’t assume specific usage patterns

3. Temporal Stability: Avoid storing derived or time-dependent values

4. Single Source of Truth: Calculate rather than duplicate

5. Incremental Adoption: Start lightweight, expand as needed

6. Philosophical Grounding: Layer 3 rooted in systematic frameworks

The framework provides architectural principles and reference specifications—not rigid templates or mandatory workflows. Adapt it to your context, needs, and constraints.

Case Studies¶

See the J-Editorial framework in action through real-world implementations:

About the Framework¶

J-Editorial was designed and developed by Josué Guevara as a solution to the limitations of traditional content management systems. Drawing on systematic philosophical frameworks, the system provides a pragmatic yet theoretically grounded approach to knowledge management and editorial workflows.

The framework emerged from real-world needs in personal knowledge management, technical documentation, and compliance-heavy publishing workflows. It represents years of iteration toward a system that is both philosophically coherent and practically useful.

Learn More¶

For updates on J-Editorial, follow development on GitHub or check back on this website for announcements.