Knowledge Mobilization

The Challenge

Siloed Knowledge & Extractive Models

Important insights get stuck in academia, separate sectors, and regions, never reaching those who could use them. Traditional models treat knowledge as proprietary, creating barriers rather than bridges.

Reinventing Wheels: Without shared learning, organizations waste time duplicating efforts.
Knowledge Extraction: Researchers take insights from communities without reciprocity or benefit.
Inaccessible Insights: Reports sit behind paywalls or on shelves, never translated into action.

The challenge: how do we create a shared ecosystem where knowledge flows freely, is co-governed by communities, and translates into real-world impact?

Our Approach

Digital Gardens & Collective Intelligence

We build living knowledge ecosystems -not static libraries, but collaborative spaces where insights evolve:

Open Sharing Accelerates Innovation

When organizations freely share successes and failures, everyone builds on each other's work, avoiding redundancy and dramatically increasing speed and scale of impact.

Diverse Knowledge Sources

Combining knowledge across disciplines, cultures, and sectors yields more robust solutions. Merging academic findings with local wisdom and AI analytics reveals insights no single perspective could.

Co-Governed by Communities

Applying OCAP® and CARE principles - communities control their own data and how it's used. Data sovereignty ensures trust and prevents extractive practices.

AI-Augmented Sensemaking

Using AI to scan vast datasets and find patterns, but always with human oversight. Hybrid intelligence - AI plus human collaboration - outperforms either alone.

Interventions

Platforms & Networks

Open Knowledge Platforms
Digital hubs organized by evolving topics (e.g., Climate Action Knowledge Commons). Shared code repositories, case studies, policy templates, and practitioner forums. Example: wildfire risk AI model shared openly between Nordic and First Nations communities.
Cross-Border Learning Networks
Connecting AI experts, social innovators, and policymakers across countries (Canada, Finland, Kenya) through virtual roundtables and shared knowledge bases. Tools and protocols shared under open licenses.
Data Commons & Sovereignty
Housing Data Commons aggregating data with governance councils including community representatives. Indigenous Data Sovereignty projects where communities define how language and cultural knowledge are shared or protected.
Digital Garden Curation
Living documents evolving over time: Seed (new idea), Bud (evidence gathering), Evergreen (validated insight). Systems Change Field Guide openly licensed, with failure reports shared candidly.

Complexity

Managing Quality & Trust

Building a knowledge commons requires balancing openness with quality, access with sovereignty:

Quality Control: Community moderators and experts review content, balancing openness with reliability without heavy-handed gatekeeping.
Avoiding Overload: AI-assisted sensemaking and human curation help users navigate vast information without being overwhelmed.
Data Sovereignty: Tiered access (public, trusted network, permission-required) with governance committees approving sharing to protect sensitive information.
Preventing Enclosure: Open licenses encourage broad uptake while community norms and reputational pressure prevent exploitation.

We treat the Knowledge Commons as a living system requiring constant attention -ensuring it remains accurate, inclusive, secure, and alive.

Learning

What We're Learning

Impact on Outcomes: Tracking cases where shared knowledge directly contributed to solutions, measuring time/cost saved by not duplicating efforts.
Community Empowerment: Do communities feel more agency when accessing data and research previously behind paywalls? Do new leaders emerge using commons resources?
Digital Garden Model: Testing what content is most useful at each stage (seedling/bud/evergreen), refining curation to avoid information overload.
AI's Role: Finding the sweet spot for AI assistance in sensemaking. When does algorithmic insight lead to genuine human insight vs. just noise?
Lab-to-Land: How knowledge translates to on-the-ground action. Do practitioners trust external knowledge? Do they prefer peer calls over documents?