One of the most common ways we talk about research is also one of the most misleading. We often imagine knowledge traveling along a straight path: a researcher makes a discovery, a journal publishes the findings, a communicator explains the results, and decision-makers apply the evidence. It is a comforting picture because it is orderly and efficient. Unfortunately, it rarely reflects how knowledge actually moves through the world.
The traditional “pipeline” model assumes that knowledge exists in a finished form, waiting only to be distributed. If more people understood the science, the thinking goes, better decisions would naturally follow. Sometimes that is true. More often, however, understanding is far more complicated. People interpret information through their own experiences. Organizations operate within practical constraints. Communities contribute local knowledge that researchers may never have considered, and professionals adapt recommendations to fit the realities of their work. Knowledge changes as it moves—not because the science itself changes, but because understanding grows through interaction.
When people hear the phrase research translation, they often think about simplifying complex ideas for broader audiences. Clear communication is certainly part of the process, but translation is much richer than simplification. It involves dialogue, questions, testing ideas, reconciling different perspectives, recognizing uncertainty, and learning from implementation. Each of these interactions contributes to a more useful understanding of the original research. Translation, therefore, is not about reducing complexity. It is about making complexity navigable.
This perspective has become central to how I think about Design Ecology. Rather than viewing knowledge as something that flows down a pipeline, I see it as moving through an ecosystem. Researchers, practitioners, educators, community leaders, journalists, policymakers, technology platforms, and citizens all play important roles. Every interaction reshapes the meaning of what came before. Instead of asking how we can preserve information unchanged, perhaps the better question is how we can help knowledge evolve in ways that remain scientifically grounded while becoming increasingly useful.
This shift has important implications for how we approach today’s most complex challenges. Climate adaptation, artificial intelligence, public health, disaster preparedness, food systems, and education do not suffer primarily from a lack of information. In each of these areas, valuable research already exists. The challenge is helping diverse groups make sense of that knowledge together. That requires more than effective communication. It requires trust, curiosity, humility, and a willingness to recognize that expertise exists in many forms.
Researchers contribute scientific expertise. Communities contribute lived experience. Practitioners contribute operational knowledge. Decision-makers contribute an understanding of political, organizational, and economic realities. None of these perspectives is sufficient on its own, but together they create something more valuable than information alone. They create understanding.
Perhaps the most significant implication of this perspective is that it changes the role of the researcher. Researchers are not simply producers of knowledge who hand completed work to others for implementation. Instead, they become participants in an ongoing conversation. Listening becomes as important as explaining. Questions become as valuable as answers. Implementation becomes another form of discovery. In many ways, knowledge does not stop evolving once a paper is published. That is often when some of its most important evolution begins.
The future will demand collaboration across disciplines, organizations, and communities unlike anything we have experienced before. That collaboration will depend on more than simply sharing information. It will depend on creating environments where knowledge can be interpreted, challenged, refined, and applied collectively.
Knowledge doesn’t move in a straight line because human systems don’t work in straight lines. Understanding emerges through relationships.