Layer 2: Dimensions

What Artifacts MEAN¶

Layer 2 defines extrinsic dimensions—calculated properties that interpret what artifacts mean in specific contexts. Unlike Layer 1 properties stored in frontmatter, Layer 2 dimensions are computed on-demand in dashboards and queries.

The Key Insight¶

Same artifact, different meanings:

A research note with refinement: 0.65 might be:

• Ready for personal reference

• Usable for internal lab sharing

• Insufficient for external publication

The intrinsic property (refinement: 0.65) doesn’t change. The extrinsic dimension (usefulness) depends on context (who’s the audience, what’s the quality gate).

This separation enables context-aware knowledge management without duplicate metadata.

16 Extrinsic Dimensions¶

Dimensions are organized into 5 categories:

State Dimensions¶

Health¶

Purpose: Overall artifact quality
Calculation: Based on refinement score and stubs count

Example query:

SELECT title, refinement, stubs_count
FROM notes
WHERE health IN ('poor', 'critical')
ORDER BY refinement ASC

Usefulness¶

Purpose: Fitness for intended audience
Calculation: refinement vs audience-specific quality gate

Usefulness values:

Example implementation:

def calculate_usefulness(refinement, audience):
    gates = {
        'personal': 0.30,
        'internal': 0.60,
        'public': 0.80,
        'published': 0.90
    }
    
    gate = gates[audience]
    delta = refinement - gate
    
    if delta >= 0.10:
        return 'ready'
    elif delta >= 0:
        return 'usable'
    elif delta >= -0.20:
        return 'developing'
    else:
        return 'insufficient'

Compliance Fit¶

Purpose: Alignment with standards, policies, or requirements
Calculation: Based on refinement, stubs, required metadata presence

Example: Publication Requirements

Trajectory Dimensions¶

Drift¶

Purpose: How much has the artifact’s alignment with its original purpose decayed?
Calculation: Based on time since creation, edit frequency, refinement changes

Example query:

SELECT title, days_since_edit, refinement
FROM notes
WHERE drift IN ('significant', 'severe')
  AND form = 'evergreen'
ORDER BY days_since_edit DESC

Health Trend¶

Purpose: Is quality improving, stable, or declining?
Calculation: Compare recent refinement changes

Example dashboard:

Network Dimensions¶

Network Position¶

Purpose: What role does this artifact play in the knowledge graph?
Calculation: Based on incoming/outgoing links, citation count

Example network analysis:

Propagation Risk¶

Purpose: If this artifact has errors, how many others are affected?
Calculation: Network position + health + outgoing links

Example risk matrix:

Workflow Dimensions¶

Stage¶

Purpose: Where is this artifact in its lifecycle?
Calculation: Based on form, refinement, stubs, workflow context

Standard workflow stages:

Example workflow dashboard:

Blocking Status¶

Purpose: Is progress on this artifact blocked?
Calculation: Based on stubs content, dependency tags, external factors

Implementation example:

# Blocked artifact
---
refinement: 0.60
stubs:
  - "BLOCKED: Awaiting ethics approval"
  - "Need institutional access to dataset"
blocking_status: blocked  # calculated
---

Priority Dimensions¶

Attention Priority¶

Purpose: What should I work on next?
Calculation: Synthesis of health, usefulness, drift, network position, blocking status

Example priority calculation:

def calculate_attention_priority(note):
    score = 0
    
    # Health component
    if note.health in ['critical', 'poor']:
        score += 3
    elif note.health == 'fair':
        score += 2
        
    # Network component
    if note.network_position == 'hub':
        score += 2
    elif note.network_position == 'connector':
        score += 1
        
    # Drift component
    if note.drift in ['significant', 'severe']:
        score += 2
    elif note.drift == 'moderate':
        score += 1
        
    # Usefulness component
    if note.usefulness == 'insufficient':
        score += 2
    elif note.usefulness == 'developing':
        score += 1
        
    # Blocking component
    if note.blocking_status == 'blocked':
        score += 1
        
    # Map score to priority
    if score >= 7:
        return 'critical'
    elif score >= 5:
        return 'high'
    elif score >= 3:
        return 'medium'
    elif score >= 1:
        return 'low'
    else:
        return 'defer'

Priority dashboard example:

Retention Value¶

Purpose: Should I keep or delete this artifact?
Calculation: Based on network position, age, refinement, recent access

Example retention analysis:

Why Layer 2 Matters¶

Without Layer 2: You have properties but no interpretation
With Layer 2: Properties become actionable insights

The same artifact can be interpreted differently in different contexts. A research note at refinement: 0.65 might be “ready” for personal use but “insufficient” for public sharing—all without storing duplicate metadata.

Layer 2 transforms static metadata into dynamic knowledge management.

Next Steps¶

• Layer 3: Learn how dimensions trigger automated workflows and policy enforcement

• Framework Overview: Return to J-Editorial

• Practice: See Layer 2 calculations in action in the Case Study