> For the complete documentation index, see [llms.txt](https://oecd-media-support-principles.gfmd.info/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://oecd-media-support-principles.gfmd.info/toolkit/principle/6-invest-in-knowledge-and-learning.md).

# 6️⃣ Invest in knowledge and learning

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### How to use this section

* For a quick self-assessment, see [**Checklist: Invest in Knowledge and Learning**](/toolkit/principle/1-do-no-harm/checklist-do-no-harm.md)
* For practical examples and applications, see [**Case studies and field insights**](/toolkit/case-studies-and-field-insights.md)
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#### In brief

Principle 6 focuses on ensuring that support to independent media and media development is **informed by evidence, strengthened through learning, and continuously adapted based on experience**.

In practice, this means recognising that knowledge - including research, monitoring, evaluation, and field-based insight - is often **fragmented, inaccessible, or underused**. Learning is frequently treated as a reporting requirement rather than a tool for improving interventions.

When knowledge is not shared or applied, support risks being based on **assumptions rather than evidence**, leading to duplication, inefficiencies, and limited impact. Investing in knowledge systems helps ensure that interventions are **responsive, accountable, and grounded in how media ecosystems function in practice**.
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### Why this principle matters

Media organisations and media development actors operate in **complex and rapidly evolving environments**, shaped by political change, technological disruption, and shifting market conditions. Effective support requires understanding **what works, for whom, and under what conditions**.

Emerging approaches are also being developed to assess the potential real-world impact of misinformation, helping fact-checkers and other actors prioritise claims based on their likelihood to cause harm. Early trials suggest that such tools can improve decision-making by focusing limited resources on the most consequential information risks. ([*see Cunliffe-Jones, P. (2025)*](https://camri.ac.uk/blog/2025/04/02/trial-finds-predictive-model-helps-fact-checkers-identify-false-claims-with-potential-to-cause-harm/)*)*

However, evidence is often fragmented across programmes, donors, and institutions. Evaluations may be commissioned but not shared, and learning may remain confined to internal reporting systems ([*see case study 7: coordination challenges and joint responses in crisis contexts*](/toolkit/case-studies-and-field-insights/case-study-7-coordination-challenges-and-joint-responses-in-crisis-contexts.md)). This limits the ability of actors to **build on existing knowledge, avoid repetition, and adapt approaches over time**.

Across the sector, organisations also report that existing research and learning are not consistently used to inform programme design and decision-making, further limiting the effectiveness of support.

Coordination challenges can further limit the effectiveness of learning, particularly where knowledge-sharing mechanisms are weak, incentives for collaboration are limited, or learning remains confined within individual programmes.

At the same time, knowledge systems are not neutral. If designed primarily for accountability, they can create **administrative burden without improving practice**. If designed well, they can support **adaptive management, strengthen decision-making, and improve outcomes across the system**.

Applying this principle helps ensure that knowledge becomes a **shared resource**, supporting both accountability and continuous improvement.

### Where this shows up in practice

This principle is most visible in how knowledge, research, and learning are integrated into programme design, implementation, and adaptation:

* **Monitoring, evaluation, and learning (MEL) frameworks and requirements** may prioritise reporting over learning, limiting their usefulness for decision-making
* **Research commissioning and dissemination practices** may not ensure that findings are accessible or used by relevant stakeholders
* **Data collection, management, and protection practices** may not be clearly linked to programme objectives, or may create risks for sensitive information
* **Knowledge-sharing mechanisms across programmes and actors** may be fragmented, reducing opportunities for collective learning and coordination
* **Integration of learning into programme design and adaptation** may be limited, affecting the ability to respond to changing conditions

These challenges often arise when **learning** is **treated primarily as a compliance requirement** rather than a management tool, when **research and evaluation findings are not** **systematically used**, or when data is collected **without a clear purpose or feedback** into decision-making. How knowledge and learning are prioritised directly affects the **relevance, adaptability, and effectiveness of support**. 

### What this looks like in practice

**Do**

* Integrate **learning and adaptation** into programme design, not only evaluation stages
* Ensure research and evaluation findings are **accessible, usable, and shared** with relevant stakeholders, including local actors
* Align monitoring and evaluation approaches to support **both accountability and learning**
* Use a combination of **quantitative and qualitative data**, including field-based insights
* Support local actors to **participate in and lead research, evaluation, and knowledge production** ([*see case study 6: local leadership and adapting compliance frameworks*](/toolkit/case-studies-and-field-insights/case-study-6-local-leadership-and-adapting-compliance-frameworks.md))

**Avoid**

* Treating monitoring and evaluation as a **compliance exercise disconnected from decision-making**
* Commissioning research or evaluations that are **not shared or not used in practice**
* Creating data collection requirements that **increase burden without clear value**
* Relying exclusively on external consultants without incorporating **local knowledge and expertise**
* Designing knowledge systems that **exclude media organisations and media development actors as users of evidence**

**Consider**

* How learning can be embedded in **ongoing programme management and adaptation**
* Whether knowledge generated is **accessible to those who can use it, including local actors**
* How to balance **data collection with proportionality and partner capacity**
* What mechanisms are needed to ensure that lessons are **shared across programmes and actors**
* How to protect **sensitive data and sources**, particularly in high-risk environments

> **Field insight**
>
> “Lessons are often collected, but not shared or used. We keep repeating similar approaches because we don’t have a clear way of learning across programmes.”
>
> *- Media development practitioner*

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### Key takeaway

Knowledge and learning are not by-products of media support. They are **core components of effective and adaptive interventions**.

When evidence is shared and applied, support becomes more responsive, efficient, and impactful. When it is fragmented or underused, interventions risk **repetition, inefficiency, and missed opportunities for improvement**.
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**For practical examples of how coordination and funding structures affect knowledge-sharing and learning across programmes, see:**

* [Case study 7: coordination challenges and joint responses in crisis contexts](/toolkit/case-studies-and-field-insights/case-study-7-coordination-challenges-and-joint-responses-in-crisis-contexts.md)
* [Case study 6: local leadership and adapting compliance frameworks](/toolkit/case-studies-and-field-insights/case-study-6-local-leadership-and-adapting-compliance-frameworks.md)

**For further reading:**

* **Cunliffe-Jones (2025)** – [*Fake News – What’s the Harm?*](https://camri.ac.uk/blog/2025/04/02/trial-finds-predictive-model-helps-fact-checkers-identify-false-claims-with-potential-to-cause-harm/) and University of Westminster / University of Wisconsin-Madison trial on assessing the real-world impact of misinformation
* **GFMD (2022)** - [Coordinating media assistance and journalism support efforts](https://gfmd.info/briefings/coordinating-media-assistance-journalism-support/). Report analysing the scope and focus of media assistance coordination efforts, highlighting common pitfalls as well as best practice models.
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### How this links to other principles

* [**Principle 1 (Do no harm):**](/toolkit/principle/1-do-no-harm.md) Learning helps identify and mitigate risks and unintended consequences
* [**Principle 2 (Increase funding):**](/toolkit/principle/2-increase-the-amount-and-effectiveness-of-funding.md) Evidence supports better allocation and design of funding
* [**Principle 3 (Whole-of-system):**](/toolkit/principle/3-adopt-a-whole-of-system-approach.md) Knowledge is essential to understanding system dynamics and interactions
* [**Principle 4 (Local ownership):**](/toolkit/principle/4-support-local-leadership-and-ownership.md) Local actors should be contributors to and users of knowledge
* [**Principle 5 (Coordination):**](/toolkit/principle/5-improve-coordination.md) Shared learning reduces duplication and strengthens coherence
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