Stage 3: Inferencing & Sense-making

Purpose & Post-Intensive Synthesis

The Inferencing stage represents the culminating phase of our evaluation framework, where the rich data collected during the Reading stage is synthesized into meaningful insights that inform future action. While Reading captures the dynamic pulse of capital flows as they happen, Inferencing transforms this data into a coherent narrative of systemic evolution.

The primary purposes of the Inferencing stage include:

1. Synthesizing the diverse data streams into coherent patterns and insights

2. Making visible the multi-capital flows that have occurred throughout the journey

3. Identifying emergent properties and unexpected outcomes

4. Creating a shared understanding of the collective’s evolutionary trajectory

5. Informing future actions, adaptations, and interventions

This stage moves beyond simple data analysis to true sense-making-a collaborative process of meaning-making that honors both the quantitative and qualitative dimensions of the evaluation data.

AI-Enabled Analysis & Timelines

Pattern Recognition

Our AI-assisted analysis tools help identify patterns that might not be immediately visible:

• Capital Flow Networks: Visualizing how different forms of capital moved between participants and activities

• Conversion Pathways: Mapping how one form of capital transformed into another

• Intensity Mapping: Highlighting where capital flows were most concentrated or diluted

• Temporal Evolution: Tracking how patterns shifted over time throughout the journey

Multi-dimensional Timelines

The evaluation data is organized into interactive timelines that show:

• Capital Flows Over Time: How different capitals ebbed and flowed throughout the journey

• Critical Moments: Key events that triggered significant shifts in capital flows

• Sphere Evolution: Changes across the five systemic spheres (cultural, economic, social, political, ecological)

• Narrative Threads: Stories that emerged and evolved throughout the journey

Inferencing Algorithms

Our AI tools employ several approaches to derive meaning from the data:

• Natural Language Processing: Analyzing textual and transcribed voice data for themes and patterns

• Network Analysis: Mapping relationships between participants, capitals, and activities

• Sentiment Analysis: Tracking emotional dimensions of capital flows

• Anomaly Detection: Identifying unexpected patterns that might indicate emergent properties

Retrospective Workshops and Adaptation Cycles

Collective Sense-Making Sessions

The heart of the Inferencing stage is collaborative interpretation through facilitated workshops:

• Data Walks: Participants physically move through visualizations of the evaluation data

• Pattern Harvesting: Collective identification of meaningful patterns and their implications

• Story Circles: Sharing narratives that give meaning to the quantitative data

• Cross-Pollination: Connecting insights across different aspects, capitals, and spheres

Adaptation Dialogues

These insights then inform structured conversations about adaptation:

• Learning Integration: How insights can be incorporated into future actions

• System Redesign: Identifying opportunities to enhance capital flows

• Barrier Removal: Addressing obstacles to regenerative development

• Potential Amplification: Recognizing and strengthening positive patterns

Documentation and Knowledge Management

The outcomes of these processes are captured in various forms:

• Evolution Narratives: Stories that capture the journey’s developmental arc

• Capital Flow Maps: Visual representations of how value moved through the system

• Learning Portfolios: Collections of insights, questions, and adaptations

• Future Scenarios: Potential pathways forward based on observed patterns

Integration with Future Cycles

The Inferencing stage doesn’t represent an endpoint but rather a bridge to future action-learning cycles:

• Baseline Refinement: Insights inform more nuanced baselines for future journeys

• Methodology Evolution: The evaluation approach itself evolves based on what was learned

• Capacity Development: Participants develop greater ability to see and work with capital flows

• Network Strengthening: Relationships formed through the evaluation process create stronger foundations for future collaboration

By moving through these three stages-Baselining, Reading, and Inferencing-our evaluation methodology creates a continuous learning loop that enhances the regenerative capacity of the communities we serve, making visible the often-invisible flows of value that drive systemic evolution.