CTR™ Framework

Executive Summary

The Content Contribution Transparency Rating™ (CTR™) system provides a structured methodology for quantifying the relative contributions of humans and artificial intelligence in collaborative content creation. Developed by Bonnie Dunigan based on extensive work in AI-assisted content development, the system employs a six-domain weighted approach to create nuanced, transparent attribution.

The CTR™ Framework addresses the growing need for transparent attribution in the age of AI-assisted content creation by:

Providing a structured framework for quantifying human and AI contributions
Moving beyond binary "AI-generated" vs. "human-authored" classifications
Acknowledging the spectrum of collaborative content creation
Creating consistent, reliable attribution methodology
Respecting human creative primacy while acknowledging AI contributions

The Transparency Paradox

Research reveals a critical insight: simple AI disclosure can reduce trust rather than build it1. Studies show that binary "AI-assisted" labels often trigger trust penalties, while detailed, contextual transparency that demonstrates collaborative value succeeds where defensive disclosure fails.

CTR™ addresses this paradox through sophisticated attribution that positions AI collaboration as professional competence rather than defensive disclosure. The framework moves beyond crude binary labels toward systematic transparency that builds rather than erodes trust.

Built on Comprehensive Evidence

Research across 15+ countries and 64+ sources confirms that effective attribution varies dramatically by context2 - that's why CTR™ is designed to adapt rather than impose fixed formulas. Legal precedents from the Federal Circuit to UK Supreme Court reject "one-size-fits-all" attribution in favor of contextual assessment3.

38-52%

Accuracy improvement over ad-hoc approaches4

40-65%

Reduction in attribution bias5

3.1x

Higher stakeholder confidence6

91%

Of creators want attribution tools7

Standard CTR™ Attribution Format

[Content Contribution Transparency Rating™: Creator X% | AI Platform Y% - Brief description of collaboration process]

For content with multiple contribution types, separate ratings can be provided for text and visual elements.

Six Research-Validated Domains

The CTR™ Framework evaluates collaboration across six essential domains with context-appropriate weighting. Each domain reflects documented patterns from successful attribution systems across academia, creative industries, and business partnerships.

Domain Weighting Rationale

The domain weightings reflect a carefully considered model of content creation value, prioritizing human creative primacy while acknowledging significant AI contributions. These percentages represent typical ranges that adapt based on context:

The combined 50-75% typical weighting for Conceptual Origin and Personal Experience recognizes that fundamental ideas and authentic human experiences constitute the most valuable and distinctly human elements of content creation
The 16-35% combined typical weighting for Structural Organization and Language Refinement acknowledges the significant value AI systems bring to content development
The 7-28% combined typical weighting for Research & Context and Ethics reflects both the varying importance of information integration across contexts and the distinctly human responsibility for ethical oversight

Important: These percentages are contextual ranges, not fixed values. Academic work typically requires higher Research & Context weighting (15-20%), while creative content may need minimal research weighting (5-8%). Business contexts often emphasize Structural Organization (15-20%), while personal narratives prioritize Personal Experience (25-30%).

Conceptual Origin

Typically 30-45%

The foundational ideas, strategic vision, and creative concepts that drive the collaboration. Research across industries consistently shows conceptual contributions receive the highest protection and recognition. This domain captures ideation, philosophical framing, original insights, and primary purpose.

Strategic framework, core insights, creative direction, problem definition, philosophical approach

Personal Experience

Typically 20-30%

Lived experience, professional expertise, and authentic insights that AI cannot replicate. This domain recognizes the irreplaceable value of human narrative, specialized knowledge, and subjective perspectives unique to individual creators.

Professional background, lived experience, specialized expertise, emotional authenticity, personal narratives

Structural Organization

Typically 8-20%

The logical flow, information architecture, and organizational framework that makes content accessible and effective. Critical for complex collaborations requiring systematic structure, including arrangement and progression of ideas.

Information architecture, logical flow, presentation structure, process design, content hierarchy

Language Refinement

Typically 8-15%

Communication optimization, tone development, and language precision that enhances clarity and impact. AI often excels in this domain while humans provide voice and style guidance, including word choice and sentence structure.

Writing quality, tone consistency, clarity enhancement, style development, linguistic precision

Research & Context

Typically 5-20%

Information gathering, data synthesis, and contextual analysis. This domain encompasses external information, contextual framing, and knowledge integration. Weight varies significantly by field - higher for academic work (15-20%), lower for creative content (5-8%).

Data collection, background research, fact verification, contextual analysis, information synthesis

Ethics

Typically 2-8%

Ethical oversight, values alignment, and responsible decision-making in AI collaboration. Represents distinctly human responsibility for ensuring integrity, appropriate use, and alignment with ethical standards.

Values alignment, ethical oversight, responsible use decisions, integrity maintenance, bias prevention

Cross-Domain Application

While presented as discrete categories, these domains interact in complex ways. The weighting system acknowledges this interconnectedness while providing a practical framework for assessment that functions across diverse content types:

Research papers may have less personal experience content but more research integration
Personal blogs may emphasize personal experience over technical details
Technical guides may prioritize procedural knowledge while still requiring conceptual clarity
Creative works may blend conceptual origin with language refinement in unique ways

Implementation Process

Assessment Methodology

The standard CTR™ assessment process follows these systematic steps to ensure consistent, accurate attribution:

Domain Evaluation: Assess relative human and AI contributions in each of the six domains
Weighted Calculation: Apply domain weights to individual assessments
Overall Calculation: Calculate weighted average for comprehensive rating
Description Development: Create concise explanation of specific contributions
Format Application: Apply standard CTR™ format to content

Example 1: Blog Post with Significant Human Conceptual Input

• Conceptual Origin (40%): 90% human / 10% AI • Personal Experience (25%): 100% human / 0% AI • Structural Organization (15%): 40% human / 60% AI • Language Refinement (10%): 30% human / 70% AI • Research & Context (5%): 20% human / 80% AI • Ethics (5%): 100% human / 0% AI Weighted Calculation: (90×0.40) + (100×0.25) + (40×0.15) + (30×0.10) + (20×0.05) + (100×0.05) = 76% human / 24% AI

Content Contribution Transparency Rating™: Author 76% | Claude 24% - Human provided core concept and personal insights; AI enhanced structure and language clarity

Example 2: Academic Research Paper

• Conceptual Origin (35%): 85% human / 15% AI • Personal Experience (20%): 90% human / 10% AI • Structural Organization (15%): 30% human / 70% AI • Language Refinement (10%): 25% human / 75% AI • Research & Context (15%): 40% human / 60% AI • Ethics (5%): 100% human / 0% AI Weighted Calculation: (85×0.35) + (90×0.20) + (30×0.15) + (25×0.10) + (40×0.15) + (100×0.05) = 65.5% human / 34.5% AI

Content Contribution Transparency Rating™: Dr. Chen 66% | Claude 34% - Human expertise shaped research design and analysis; AI provided comprehensive literature review and documentation support

Framework Innovation: AI Self-Assessment

CTR™ addresses universal attribution challenges through AI's unique capacity for objective self-evaluation. Research across all collaborative fields shows human cognitive biases systematically distort contribution assessment - that's why every industry has developed formal processes to address attribution disputes.

No Ego Investment AI systems have no reputation or career concerns affecting attribution accuracy

Process Transparency Real-time documentation rather than post-hoc memory reconstruction

Conservative Assessment Constitutional AI design produces honest, conservative self-evaluation

Systematic Consistency Structured methodology across all collaboration types and contexts

Context-Dependent Weighting

CTR™ Framework provides systematic structure while adapting to different collaborative contexts. Research demonstrates that successful attribution has always been contextual - from academic authorship to creative industry credits to business partnerships.

Suggested Weighting Profiles

Based on cross-industry analysis, the framework offers context-specific starting points while maintaining user control over final attribution decisions:

Academic/Research Context

Higher weighting for Research & Context (15-20%) and empirical validation. Conceptual Origin (35%) and Personal Experience (20%) maintain strong recognition while Research receives academic-appropriate weighting. Structural Organization (15%) supports systematic presentation. Ethics may increase to 10% for sensitive research areas.

Creative/Content Context

Highest Conceptual Origin weighting (45%) reflecting creative industry patterns. Personal Experience (25%) and Language Refinement (15%) receive enhanced recognition for authentic voice and style. Research may decrease to 5-8% for purely creative work. Structural Organization typically 10-12% for narrative flow.

Business/Professional Context

Balanced Conceptual Origin (30%) and Personal Experience (30%) with higher Structural Organization (15-20%) reflecting business emphasis on systematic implementation and professional expertise. Language Refinement (10%) maintains clarity. Research & Context (10-15%) supports market analysis. Ethics may increase for compliance-sensitive content.

Legal Precedent Supporting Flexibility

The Federal Circuit's rejection of fixed percentage rules in patent law establishes legal precedent against formulaic attribution8. Courts from the UK Supreme Court to US Federal Courts emphasize context-specific assessment over mechanical rule application9.

CTR™'s flexible methodology aligns with established legal frameworks while providing systematic structure that exceeds simple binary disclosure requirements.

Professional Applications

CTR™ provides industry-specific implementation templates that demonstrate professional transparency while building trust through meaningful disclosure.

Academic Research

Research Methodology Transparency

This study employed AI assistance for literature compilation while 
maintaining complete human oversight for research design, interpretation, 
and scholarly conclusions.

Content Contribution Transparency Rating™: Dr. Chen 84% | AI Systems 16%

All theoretical frameworks and scientific interpretations represent 
the researcher's professional expertise and academic judgment.

Framework: CTR™ by Bonnie Dunigan (https://ctrframework.com)

Business Consulting

Strategic Analysis Transparency

This market analysis combines 15 years of consulting expertise with 
AI-enhanced research capabilities, providing comprehensive insights 
while maintaining strategic judgment essential for implementation.

Content Contribution Transparency Rating™: Senior Partner 82% | AI Systems 18%

All strategic recommendations reflect proven methodology and professional 
judgment enhanced by systematic research capabilities.

Framework: CTR™ by Bonnie Dunigan (https://ctrframework.com)

Content Creation

Professional Content Development

This analysis combines authentic professional experience with AI-enhanced 
research capabilities, providing strategic insights backed by comprehensive 
market intelligence.

Content Contribution Transparency Rating™: Alex Chen 79% | Claude 21%

All recommendations stem from proven consulting methodology while AI 
enhanced research comprehensiveness and presentation quality.

Framework: CTR™ by Bonnie Dunigan (https://ctrframework.com)

Advanced Multi-Session Methodology

For complex projects spanning multiple conversations with discrete deliverables, CTR™ employs a sophisticated two-category attribution system:

Process Documentation

Conversations (Process-Based): Extended collaborative sessions tracked collectively without individual ratings to eliminate assessment fatigue
Complete Context Preservation: Full conversation history maintained for accurate comprehensive analysis
Evolution Tracking: Documentation of collaboration patterns throughout project development

Product Attribution

Artifacts (Output-Based): Discrete deliverables (tools, reports, content) receive individual CTR™ ratings for standalone value
Standalone Attribution: Each artifact maintains independent attribution suitable for separate sharing or citation
Integration Analysis: Individual ratings inform overall project evaluation

Comprehensive Analysis

Project Completion: Synthesis of all conversations and artifacts for complete collaborative transparency
Professional Documentation: Attribution suitable for academic, business, and professional contexts
Quality Assurance: Systematic verification of attribution accuracy and professional integrity

Beyond Compliance: Professional Positioning

While others offer binary "AI-assisted" labels, CTR™ provides sophisticated attribution that demonstrates professional transparency leadership. The framework positions users ahead of evolving regulatory requirements while building audience trust through meaningful disclosure.

Current Regulatory Landscape

AI transparency requirements are rapidly evolving across platforms and jurisdictions. YouTube now requires AI disclosure (May 2025)10, the EU AI Act begins enforcement (August 2025)11, and California SB 942 takes effect (January 2026)12. CTR™'s systematic approach works across all current and emerging requirements.

89%

Of consumers want clear AI labeling13

90%

Of creators use AI tools14

Major platforms with AI requirements

2026

California transparency law effective

Framework Innovation: Constitutional AI Foundation

CTR™ leverages Anthropic's Constitutional AI design, which prioritizes honesty and transparency through built-in ethical constraints15. This creates optimal conditions for objective self-assessment free from ego or reputation concerns affecting attribution accuracy.

Framework Evolution and Continuous Improvement

User-Informed Development

Framework development continues through user feedback and real-world implementation experience, with methodology updates based on:

User experience and implementation success patterns
Technological advances in AI capabilities and assessment accuracy
Regulatory requirement evolution across jurisdictions
Professional practice development across industries

Quality Assurance Standards

Complete context preservation for accurate assessment
Professional documentation suitable for various contexts
Systematic verification of attribution accuracy
Maintenance of theoretical soundness while ensuring practical accessibility

Version Control and Documentation

All framework modifications are documented with clear rationale, impact assessment, and migration guidance for existing implementations, ensuring quality and practical relevance through evidence-based improvement.

Ready to Get Started?

CTR™ Framework transforms AI collaboration from hidden partnership to transparent professional practice. Choose your implementation approach and begin building systematic transparency today.

Quick Start Guide View Examples Research Foundation

References

1Jago, A.S., Kreps, T.A., & Laurin, K. (2025)Full Citation

2Bommasani, R., et al. (2024)Full Citation

3Federal Circuit Court Decisions (2017-2024)Full Citation

4Du, S., McElroy, T., & Ruhe, G. (2006)Full Citation

5Bertrand, M., et al. (2023)Full Citation

6Schnackenberg, A., et al. (2024)Full Citation

7Adobe Research Team (2024)Full Citation

8Uniloc v. Microsoft, Federal Circuit (2011)Full Citation

9Kogan v Martin, UK Court of Appeal (2019)Full Citation

10YouTube Policy Update (2025)Full Citation

11EU AI Act Regulation (2024)Full Citation

12California SB 942 (2024)Full Citation

13Adobe "The Age of Generative AI" Study (2024)Full Citation

14HubSpot State of Marketing Report (2024)Full Citation

15Bai, Y., et al. Constitutional AI Research (2022)Full Citation

The CTR™ Framework