The Different Levels of Knowledge Translation and Why They Matter to Academics Who Want to Make an Impact

At a recent research conference, I enjoyed networking with scientists and social scientists from a wide range backgrounds, all of them domains far beyond my knowledge base. As I sidled from one conversation to the next, I got glimpses into fields as diverse as psychiatry, pediatrics, molecular genetics, social services, biomedical engineering, kinesiology, biology, transportation engineering, and occupational health. 

As someone who loves to learn, I was in my element. Yet I’ll confess there were moments when I found the amount of intellectual stimulation almost overwhelming. My brain was working overtime on decoding technical terms and contextualizing topics so I could at least listen intelligently to the conversation even if I couldn’t really contribute to it.

Then, toward the end of the conference, I had an aha moment. I found myself in a conversation cluster with a molecular geneticist and a handful of other scientists, and I discovered I wasn’t the only person at the conference encountering a steep learning curve.

This realization helped me understand that one of the teaching tools I’ve been using for years, The Knowledge Spectrum, needs a revamp. Today’s science has become so complex that it’s no longer enough just to consider where your target audience sits on a continuum of technical knowledge, as shown below.

Traditional Knowledge Spectrum

In this diagram, the level of knowledge increases as you read from left to right:

• A novice is someone brand-new to a topic.
• An intermediate user is a practitioner who interacts with and applies the technology or science without understanding its inner workings.
• A technician has practical knowledge of how a piece of technology or an aspect of science works but does not create new knowledge.
• An expert knows how to design and create new technology or make scientific discoveries.

While this traditional scale works well in standard situations, such as a consulting engineer communicating with a client or a science journalist writing a blog post, it’s time for an upgrade. Given the pace and complexity of science today, we now need more sophisticated tools to think through the difficulty inherent in a piece of knowledge and consider how that maps to the audience’s capacity for understanding.

This distinction is critical for researchers and R&D teams who aren’t just opening doors to new insights but are blasting doors off their hinges. If that’s your situation, then the communication challenges you face don’t really fit under the rubric of traditional technical communication, science communication, or knowledge translation. You’re engaged in what I call “innovation communication,” and that means you must work harder to connect successfully with audiences such as funders, policymakers, practitioners, and customers.

Esoteric subjects require new communication strategies

Let’s get back to my conversation with the molecular geneticist. As they explained to me, a complete novice, what their research involved, they joked that few people at the conference could readily understand their work. Molecular genetics, they said, is a topic so complex that even doctors, biologists, and scientists in adjacent fields have trouble grasping it.

Nods around the group confirmed this communication dilemma. So I turned to the scientist standing next to me, a kinesiologist who studies the effect of exercise on health, and asked whether they faced the same degree of difficulty in sharing their research with nonexperts.

The answer came without a moment’s hesitation: “No, most people get the general idea of what I do.”

“Kinesiology” may not be a household word, but most people these days recognize that exercise affects physical well-being (even if many of us don’t put that knowledge into daily practice).They understand, therefore, that someone might study the different effects of different kinds of exercise on different people. They may not be able to interpret the data, but they get the general drift of the research activity.

That doesn’t mean that Professor Kinesiology doesn’t face challenges in translating their academic knowledge into language and visuals that resonate outside the lab. But Professor Molecular Genetics faces a far greater challenge because knowledge in their field isn’t just-out-of-reach of the audiences they communicate with. It’s so complex, so unfamiliar that I’d describe it as arcane: mysterious to all but a select group of the initiated.

When you’re dealing in the esoteric, you can’t rely on the simple, one-dimensional strategies used to share generic technical information. You need to think in at least two dimensions by considering not just the level of your audience’s knowledge but also the level of the knowledge you’re trying to convey and the differential between them. You must take into account the level of knowledge translation you’re undertaking.

Traditional approaches don’t fit emergent fields

“Know your audience.” “Conduct a detailed audience analysis.” “Put yourself in your audience’s shoes.”

These are all phrases I’ve repeated hundreds of times, to hundreds of students, over the past 20-plus years as I’ve taught communication to engineers, software developers, and scientists. While such audience-centric advice is generally sound, I now see that in some circumstances it may make sense to go against conventional wisdom and start with the subject matter.

When an entire field is new and the knowledge being generated is still emergent, then the best place to begin may be the topic itself. In such a case, it’s useful to ask yourself questions such as these:

• How long has my field of inquiry been considered a knowledge domain in its own right?
• To what extent is the general public aware of my field of inquiry?
• In a room of scientists from adjacent fields, how many of them would recognize my specialized vocabulary?
• How much of that vocabulary existed 20 years ago?
• How easy is it to express my specialized knowledge through analogies to everyday things or processes?
• How easy is it to convey my specialized knowledge through language that’s concrete, rather than abstract?

Let’s say, for instance, that you’re a specialist in quantum biology, a field that is just finding its feet as researchers attempt to apply quantum physics to biological processes. Many people, including scientists in other specialties, are likely unaware not just of your research but also of the field within which you operate. Because the whole discipline is just emerging, it is also extra-challenging to express its tentative findings through everyday analogies and abstract language.

Contrast your situation with that of a civil engineer building bridges and roads. While many of the audiences this expert communicates with don’t understand the physics and math behind the engineering process, they’re familiar with the results of that process. They use bridges and roads in their daily life, so they don’t have to stretch far to imagine someone designing and testing those structures. They probably even use a few engineering terms in their everyday conversations, such as “friction” and “incline.”

We can place the civil engineer and you (the quantum biologist) on a hierarchy of communication difficulty based just on your topic area. In the diagram below, the engineer’s communication activities and yours constitute two different levels of what we broadly consider "knowledge translation." The engineer engages in “technical communication” while you engage in “innovation communication.”

Hierarchy of Communication Difficulty Based on Topic

In between the base and peak of the pyramid, we find two intermediary categories, which I define as follows:

- Advanced technical communication—Topics that relate to technology but incorporate new research or data. For example, a civil engineer working in a design lab or modelling flood risks would communicate about subjects more complex and esoteric than those handled by a civil engineer in a more standard situation.

- Translation of academic knowledge—The many different definitions of knowledge translation deserve an article in their own right. But here, I want to point out that, despite an overabundance of scholarly definitions, the term is becoming increasingly less precise in usage. Originally viewed as the work of scholars, knowledge translation is now being ascribed to the work of students, even undergraduates. Many of these learners are interpreting existing knowledge, not translating knowledge of their own making. (On a learning taxonomy such as Bloom’s or the SOLO scale, they aren't functioning at the top tier.) The further removed the work of “translation” becomes from the work of knowledge creation, the lower it drops on the hierarchy of communication difficulty.

You’re operating at a more advanced level of knowledge translation, which requires more refined strategies. Pat advice to “adapt to your audience,” which usually entails emphasizing high-level concepts and simplifying your communication style, just won’t do the job.

Coping without a peer group

The world of innovation can be a lonely place. If you operate in a domain of arcane knowledge, then you have few (maybe zero) compatriots among the audiences you want to impact. Recognizing that is the first step toward closing the huge chasm between you and the nonexperts you’re trying to reach.

Once we acknowledge innovation communication as a unique zone where meaning is made and explained, then we see that the traditional Knowledge Spectrum I shared earlier is missing a heading: Peer. Visualized as a pyramid, rather than a line, here’s what the revised Knowledge Pyramid looks like:

Knowledge Pyramid

The special challenges faced by some innovators now appear obvious. Whereas many technical and scientific experts form part of a sizable peer group, researchers in emerging fields don’t enjoy that luxury. As a result, even when they think they’re talking to someone like themselves, communicating expert to expert, they probably aren’t. The knowledge differential is almost certainly present, making the labor required to translate research more demanding than it is for others with similar credentials.

The diagram below makes this point visually. As you can see, for a researcher in an emerging field, there’s unlikely to be a straight line from expert to expert. (Contrast this with the situation of our civil engineer, who will likely encounter plenty of situations in which they’re communicating with another civil engineer.) The gap between researcher and the different levels of audiences thus becomes wider at every tier.

The Knowledge Differential for Researchers in Emerging Fields

Strategies for communicating from an emerging field

So let’s say you’re a quantum biologist, or a molecular geneticist, or an interdisciplinary innovator working on a solution so novel you have to invent language to describe it. What can you do to narrow the chasm between you and your target audience?

The first thing is to start at the level of strategy, not tactics. Strategy considers the big picture, how you’ll connect with the audience’s frame of reference by selecting and structuring ideas and information. Tactics are fine-tuning—simplifying language choices, streamlining sentence structure, and breaking up text into units of meaning that are easy to digest (e.g., short paragraphs, vertical lists, tables, and graphics).

Here are three strategic steps to take:

#1. Pay attention to framing. Framing provides a cognitive and emotional context for the knowledge you want to share. When you frame a topic, you select certain aspects of it to emphasize (which means de-emphasizing certain other aspects). This enables you to align your topic with your audience’s perspective, including their values and beliefs.

When you frame science, you place it in the context of situations or trends your audience is already aware of. For example, you might frame a discussion of nanomaterials by mentioning the trend toward smaller and smaller computers, requiring tinier and tinier internal components.

One of the most powerful ways to frame research findings is to link them to a human-interest story. For example, you could connect quantum biology to new healthcare treatments that could save lives.

#2. Use old terms before you introduce new language. When you’re working in an emerging field, you probably find that you need new terms to accurately convey new concepts and observations. But when you’re sharing your knowledge outside the lab, use a soft-sell approach. Rather than introducing the newly-invented term and then defining it, state the concept in language familiar to your audience, and then explain how the neologism perfectly expresses the new finding. (Did you see how I just did that in the previous sentence?)

This approach may feel counter-intuitive because it goes against the vocabulary-first way that’s often used to teach technical subjects, such as math, biology, and engineering. However, as constructivist educators have been telling us for years and cognitive scientists now affirm, learners can absorb new information only by connecting it to something they already know. Starting with old language, even if it’s not as accurate as the new, will create the surest path toward comprehension.

#3. Cultivate an attitude of hospitality. Consider how you can welcome newcomers to the emerging field you call home. While it’s important to recognize barriers to comprehension, take a positive attitude toward your audience and look for common ground. In many situations, while you and your audience may differ dramatically in your level of topic-specific language, you may share common interests, beliefs, and values.

For example, let’s say you’re working on new technology for batteries for electric vehicles. I may not understand submolecular physics, but I can easily share your desire to reduce greenhouse gas emissions and save our fragile planet.

Also use your enthusiasm for your subject to attract your audience’s interest. Let your passion for your research shine through and your audience will find it infectious even if they don’t understand every syllable you say.

Emotion is a language that speaks directly to the heart (or to the amygdala if you prefer a more scientific perspective) and provides a shortcut to persuasion. As a scientist, you’ve been trained to communicate in an impersonal style, but as you reach beyond the walls of the Ivory Tower, you can let some of your guard of objectivity down. Focus on fostering a genuine human connection with your audience, and you may be surprised by how little scientific detail it takes to get them excited about your findings.

As knowledge advances, and new research fields emerge, so must our understanding of what we’re now loosely calling “knowledge translation.” While it’s tempting to think that scientific frameworks and step-by-step methods can simplify the challenges of research communication, there’s no one-size-fits-all approach. The first, critical step is to recognize that and  gauge the level of knowledge translation you’re dealing with.

Know thy audience, yes. But know thy research area as well so you can judge how esoteric your knowledge is and determine the level of knowledge translation you're engaging in. Only then can you approach communication challenges with compassion for both your audience and yourself, and weave connective threads of genuine understanding.