Why basic research matters II: John Sivell

Currently, we hear a lot about the value of research that focuses on a manifestly practical application. This argument seems to reflect the tradition of high-level technical education that began in the period of the Industrial Revolution, and plainly there is much to recommend it. But there is also a risk in presenting this perspective: we need to avoid underestimating the complex evolutionary development of core insights that can and do have a world-changing practical impact on daily life.

I work in Linguistics, a very interdisciplinary domain that is usually homed in the Humanities. This matters to me, because I sense a general undervaluation of Humanities research and education. I trust that that misperception will eventually fall back out of fashion.

I am concerned, however with a more lasting bias against encouraging the kind of non-linear, intellectually rich environment that can foster new ideas and discoveries and even lead to practical applications. New understandings emerge quite unexpectedly, and virtually never in a predictable way.

To get a sense of the value of pure intellectual exploration, consider the very old philosophical tradition to view the world in terms of contrasts. In the early part of the 20th Century, that underlying theme surfaced in the form of Swiss linguist Ferdinand de Saussure’s brilliantly simply concept that human language could be effectively explained as a system of contrasts, so that by focusing on langue (language as an abstract system) rather than parole (individual language codes) it would be possible to understand psycholinguistic and philosophical characteristics of communication that would not be remotely so apparent to scholars concentrating just on English, French, German, and so on. That leap forward has driven the development of thinking in Linguistics and in Communication Studies.

At about the middle of the century, American mathematician Claude Shannon advanced another type of system relying on opposites: this time, a mathematical theory of electronic communication in which patterns of contrast had the capacity to provide the redundancy necessary for overcoming interference from noise on the line. His work is one of the key foundations for the communications revolution that led to the digital world on which so many of us rely today.

Unquestionably, Shannon’s discovery is a veritable poster child for practically applicable research, and it might even be taken as a foil for Saussure’s fascinating but essentially impractical line of thinking.

Shannon himself does not mention Saussure, but he does refer admiringly to the work of Norbert Weiner on cybernetics. And semiotician N. Katherine Hayles links not only Weiner with Saussure, but also Saussure with Shannon. Of course, such a circuitous provenance definitely does not constitute a tightly-linked chain of direct influences, but perhaps this is exactly the point. While curiosity-based research, scholarly thinking, and education may or may not trigger any immediate practical outcome, the constant value of such efforts is that they flow into the exciting intellectual environment in which all sorts of thinkers and researchers operate.

It is evident that Saussure himself was simply curious about language; he clearly had no intention of presenting a theory that might be commercially profitable. But by the time Shannon – who did have commercial applications in mind – came onto the scene, Saussure’s theory was ‘in the air’: available to Shannon as a very useful concept. Shannon may not even have been aware of Saussure, but he surely was aware of Saussure’s ideas.

In fine, Shannon did not work in a bubble entirely isolated from other intellectuals. And that, surely, is the major take-away from this example. The distinction between what is or is not ‘useful’ or ‘practical’ research or education depends very much on how narrowly and superficially one decides to view the matter. Using a term from another perhaps ‘impractical’-looking linguist, Michael Halliday, it is a question of delicacy, the degree of finesse with which definitions are framed: too little delicacy, and everything flows into everything else and informative categorizations become impossible; but too much, and the sledgehammer of oversimplified precision fractures the very pattern one initially set out to clarify.


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From farm to fork

ORION staff | October 9, 2015

It’s morning. Farmers across Ontario are waking up to tend to their animals. You might be sitting down to a plate of scrambled eggs—maybe even a few strips of bacon. We take it for granted that this food will be safe to eat. But we rarely think about why. That, in part, is thanks to people like University of Guelph systems design engineer Deborah Stacey. Relying on a high-performance computing network, her research helps inform the regulatory structures that ensure our food is free of contamination and that the animals it comes from are healthy. It is, in part, due to her work that we now have modelling programs such as NAADSM, the North American Animal Disease Spread Model. This is the software governments and industry rely on to plan for and prevent epidemics. “NAADSM allows you to put in various scenarios for various animal diseases to see how they would spread,” says Stacey. “My interest is in looking at the network connections within that: contact, moving animals from one herd to another, and licking or touching other animals. I’m interested in how these contact networks differ across industries, which could suggest a different path of disease spread.” This research is then used by organizations such as the Guelph-based Poultry Industry Council to help determine which transportation and feed networks most effectively limit or eliminate things like avian diseases—in other words, how to ensure your scrambled eggs are safe. Stacey’s work produces a staggering amount of data, and it requires a lot of statistical analysis. It’s done through the Shared Hierarchical Academic Research Computing Network, or SHARCNET, a consortium of Ontario universities, colleges and research centres using a shared system of distributed high-performance computing, linked together through the ORION network. “Studying these networks made me more aware of how we develop and distribute the food we eat,” Stacey says. “It was surprising to find out how critical these farming systems are, and that they can be understood using mathematical models. These human systems that we’ve evolved are incredibly complex, and it was enlightening to see how much we need to study this—our food safety and security depend on understanding these systems.” A  version of this story was originally published by ORION.  It has been edited for clarity, accuracy and brevity, and is republished here with permission. 
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