If there are still scientists toiling away with little regard for what others may think of their efforts, it's time to drag them kicking and screaming into the 21st century.
In today's interconnected world, what scientists do is of vital concern to the wider public. And vice versa. Just consider the controversies surrounding nuclear energy, genetic engineering, telephone antennas, global warming -- and even the effects of computers on education and individual liberty (surveillance society).
For people engaged in science, both pure and applied, communication with the general public is no longer an option; it is a requirement. Unfortunately, scientists (like most other people) write poorly, because they have never been taught any better. While schools emphasize creative writing (literature), they give short shrift to expository writing, i.e. effectively conveying ideas and information.
So when we sit down at the keyboard, how can we be certain that our readers will understand what we say with minimum effort and maximum interest?"
We can't. However we can greatly improve the odds by abandoning subjective ideas of what constitutes expository writing and replacing them with quasi-objective criteria.
During my 40-year career, I have relied on three such criteria, or "acid tests", that have served me very well. Not just for writing myself, but equally for evaluating the writing of others.
Many people don't actually do much writing themselves, but frequently may have to critique the writing of others. It is of very little use to tell someone that a text isn't "good enough", "interesting enough", or "just doesn't feel right". So work on it. Such fuzzy criticism is not only unhelpful; it can be positively demoralizing.
I am reminded of the story of a junior executive who presented a document he had written to his superior. He was told, "Make it more interesting". Being conscientious (and somewhat fearless), he replied: "Sir, this is the best text I know how to write based on the information I have. Unless you tell me exactly what you are looking for, any way l change it will only make it worse."
Fortunately, the man's superior recognized the wisdom of the comment. In other words, in order to critique usefully, you must be explicit. This is exactly what my three criteria allows you to do. Actually, it is incorrect to call them "criteria", because they are more than that. They are fundamental principles in the form of formulas that provide step-by-step instructions for producing recognizably well written texts, whatever the format or subject.
If you are the originator, they tell you:
1) How to write your text in the first place
2) How properly to edit it when you have finished
If you are the critic, they tell you:
1) What the text should contain
2) What needs to be done to improve it
Before looking at them in detail, let's first agree what we mean by a well written text. For most people, it has at least two principal characteristics; it must be both "clear" and "concise".
Unfortunately, both of these are "weasel words". They mean different things to different people, as well as different things at different times. This is why we need quasi-objective tests to be certain that these words will mean essentially the same thing to all people all the time.
There is a third aspect of a well written text called "density", for which we also have a quasi-test.
Test for Clarity
According to the Clarity Principle, to be clear a text must do three things:
1. Emphasize what is of key importance.
2. De-emphasize what is of secondary importance.
3. Eliminate what is of no importance.
In short: Cl = EDE
If you follow the formula, when you evaluate a text (yours or someone else's), the first thing you should look for is: Do the key ideas fully stand out?
Key ideas are the concepts and conclusions the writer wants the readers to take away from text. Too many writers shy away from the hard work of defining the key ideas. It is far simpler to say that everything is of key importance, so they put in everything they have. However, unless the writer does the job of defining what he really wants the readers to know, they won't do it for him. They will simply get lost in your text and either give up or come out the other end not knowing what they have read.
Second, check that the text de-emphasizes everything that is of secondary importance. Why? Because if you want readers to recognize and retain the key ideas, then you don't want them getting lost in the details. Details (information of secondary importance) explain and support the key ideas; they must never overwhelm them.
Finally, you must ruthlessly eliminate everything of no importance. These are bits of information that are neither a key idea nor explain or support a key idea. Nothing is neutral. Whatever doesn't add to a text, subtracts from it. And so must be deleted.
Test for Conciseness
According to the Conciseness Principle, a well written text should be as:
1. Long as necessary
2. Short as possible
In symbols: Co = LS
It is commonly claimed that people today have shorter attention spans than in the past, so texts must also be shorter. I am unaware of any scientific evidence that supports this contention. However, I am aware of considerable historical and psychological evidence that disputes it.
People pay attention to texts that catch and hold their interest. Once that interest wanes, they stop reading. It's as simple as that. Whatever effect radio, television, films, the Web, etc. may have had on average attention span, individual attention span is governed by self-interest. This has always been true, and remains so.
The objective, therefore, is not to constrain a text to fit some artificial limit, but to be certain that everything it says has purpose and meaning for the reader. This is what makes a text concise, whatever its length.
In the Conciseness Principle, "as long as necessary" means covering all the key ideas you identified under "clarity", and all the information of secondary importance needed to explain and support them. Note that nothing is said here about the number of words, because it is irrelevant. If it takes 500 words to be "as long as necessary", then 500 words must be used. If it takes 1500 words, then this is all right, too.
"As short as possible" means staying as close as you can to the minimum. Not because people prefer short texts. "Long" and "short" are weasel words; in the abstract they have no meaning because what is "long" in one circumstance is "short" in another.
The important point is: All words beyond the minimum tend to damage clarity. Subconsciously, readers will continually be trying to understand why those words are there. And will be continually failing because they serve no purpose.
Test for Density
"Density" is a less familiar concept than clarity and conciseness, but it is equally important. According to the Density Principle, a text should contain:
1. Precise information
2. Logically linked
In other words: D = PL
Using precise information rather than wishy-washy weasel words aids clarity. For example, if you say it is a "hot" day, what do you mean? One reader might interpret hot as 24� C while another might interpret is as 36� C. However, if you say the temperature outside is 28� C, there is no room for interpretation -- or misinterpretation.
Using precise information also generates confidence, because it tells the reader that the writer really knows what he is talking about. This helps to hold the reader's attention and makes it easier to get key points across.
However, precise data (facts) by themselves are insufficient. To be meaningful, data must be organized to create "information". There are two important tests to apply when converting data into information.
Is a particular piece of data really needed? As we have seen, unnecessary data damages clarity and ultimately confidence. Therefore, any data that do not either aid understanding or promote confidence should be rigorously eliminated.
The logical link between data must be made explicit to prevent readers from coming to false conclusions. Example: A singular occurrence may be misinterpreted as part of a broad pattern; a general policy may be misinterpreted as applying only in specific circumstances, etc.
To ensure that a logical link is clear, the two pieces of data should be placed as close to each other as possible, preferably right next to each other. When data are widely separated, their logical link is masked. If the writer doesn't make the logical connection, it is unrealistic to expect that readers will do so for themselves.
So there they are -- three "acid tests" for clear, concise, dense writing. Although quasi-objective, these tests are not a panacea. They require you to think; in fact, they force you to think. And that is their strength, because they guide your thinking to precisely what you should be thinking about.
According to the adage: "If you don't know what you are looking for, you are unlikely to find it even if it's right in front of your nose." Well, now you know.
Philip Yaffe is a former reporter/feature writer with The Wall Street Journal and a marketing communication consultant. He currently teaches a course in good writing and good speaking in Brussels, Belgium. His recently published book In the "I" of the Storm: the Simple Secrets of Writing & Speaking (Almost) like a Professional is available from Story Publishers in Ghent, Belgium ( http://www.storypublishers.be/) and Amazon ( http://www.amazon.com/).
For further information, contact:
Tel: +32 (0)2 660 0405
[email protected], [email protected]
Source: Ubiquity Volume 9, Issue 35 (September 23 - 29, 2008)
A Ubiquity symposium is an organized debate around a proposition or point of view. It is a means to explore a complex issue from multiple perspectives. An early example of a symposium on teaching computer science appeared in Communications of the ACM (December 1989).
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Ubiquity Symposium: Big Data
- Big Data, Digitization, and Social Change (Opening Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic
- Big Data and the Attention Economy by Bernardo A. Huberman
- Big Data for Social Science Research by Mark Birkin
- Technology and Business Challenges of Big Data in the Digital Economy by Dave Penkler
- High Performance Synthetic Information Environments: An integrating architecture in the age of pervasive data and computing By Christopher L. Barrett, Jeffery Johnson, and Madhav Marathe
- Developing an Open Source "Big Data" Cognitive Computing Platform by Michael Kowolenko and Mladen Vouk
- When Good Machine Learning Leads to Bad Cyber Security by Tegjyot Singh Sethi and Mehmed Kantardzic
- Corporate Security is a Big Data Problem by Louisa Saunier and Kemal Delic
- Big Data: Business, technology, education, and science by Jeffrey Johnson, Luca Tesei, Marco Piangerelli, Emanuela Merelli, Riccardo Paci, Nenad Stojanovic, Paulo Leitão, José Barbosa, and Marco Amador
- Big Data or Big Brother? That is the question now (Closing Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic