How scientific revolutions influence our understanding of journalism
From the miscommunications that delayed the discovery of oxygen, to the technology that limited the discovery of Uranus, scientists have encountered challenges in the same way journalists and technologists do today in the newsroom. By understanding the process of successful, influential discoveries in science, we can apply their paradigm to journalism to make better sense of its own history, current stance, and potential future.
Inspired by The Structure of Scientific Revolutions by Thomas Kuhn, 1962.
The Paradigm Structure
Defining a framework for a set of information can help organize and better understand its purpose with factual reasoning. Whether it’s how to build a paper airplane or how to create a business, structures are everywhere. They can even help us learn how a particular profession became what it is today and how it could evolve in the future, reasonably. The paradigm for science was found by Kuhn’s research in science, but is not solely unique to the science industry and can be “plugged in” to other professions.
The structure is defined by the following parts:
- Paradigm: A significant model and stage of development
- Normal science: Current practices or knowledge accepted at a given time
- Anomaly: A problem in a Paradigm, based from ideas or facts
- Theory: A suggested solution to an anomaly
To help us visualize these definitions and put them into perspective, I’ll continue with the example of paper airplanes — you’re welcome.
Imagine examining 100 paper airplanes, what patterns would you see? Basically, we know its paper is foldable, has wings, and a handle for throwing. This common, universal knowledge is defined as normal science. Now, if those planes were all thrown at once, we would likely notice differences in performance. We may see some dive straight to the ground, curve upward, or spin — these problems are defined as anomalies. Anomalies are unexpected behaviors that deter it from the goal (flying realllly far).
In response, there are a number of different theories as to why an anomaly occurred and how it could be fixed. Some people may say a plane dove because of the angle of the person’s arm, wind, weight of the paper, or shape of the wings. At this point, current normal science is in doubt and new research is taken to further advance what we know about a proper paper airplane — the prelude to discovery! There could be a number of theories suggesting solutions to prevent wind from impacting the flight — such as cutting holes in the paper, using sturdier paper, or throwing it faster. All are reasonable, but it’s not until a theory is tested and proven across many factors that it transforms from an idea to a fact. It may then become accepted by the profession for building paper airplanes, further evolving normal science. Now, future paper airplane builders would have this newly developed knowledge and could instinctively know how to avoid making a plane that dives. With a combination of anomalies resolved and theories accepted, the profession of paper airplanes would reach a new paradigm.
And that’s it…? Not quite, this process is a cycle. In this new paradigm of non-diving planes, we may see a number of new anomalies created by newly found environments (being in space without gravity…), technologies (robots that can throw…), or materials (edible paper…?). All paradigms encounter new sets of anomalies and theories that help lead it to the next paradigm.
Now that we‘ve learned the basics, I‘ll explain a little more in-depth about the process and with examples in science.
Going from one paradigm to the next is called a paradigm shift and described as “picking up the other end of the stick.” It’s a process that involves “handling the same bundle of data as before, but placing them in a new system of relations with one another by giving them a different framework.” Similar to the transition from print to digital journalism!
Led by a new paradigm, scientists adopt new instruments and look in new places. Even more important, during revolutions scientists see new and different things when looking with familiar instruments in places they have looked before.
Articulation and translation
During testing of theories, communication of the proposed solution is critical. This includes both articulation of the tools chosen and translation of its solution. These are critical in that they determine whether a theory becomes proven, understood, and accepted amongst the profession’s community.
Normal science does and must continually strive to bring theory and fact into closer agreement (empirical understanding), and that activity can easily be seen as testing or as a search for confirmation or falsification.
Kuhn suggests theories and paradigms are similar to puzzle-solving. The success of the paradigm is not reliant on merely “to make a picture [of theories],” but rather to have rules or ethics defined that help illustrate the intended picture. However, these rules are usually loosened during the transition from one paradigm to the next.
Differences in perception
The book points out many cases when a theory’s approach or message misled the community to have an unintended or detrimental perspective:
In the 1770’s, oxygen was on the brink of discovery, British chemist Joseph Priestley and France’s Antoine Lavoisier were head-to-head with their own theories. Priestley announced that he had discovered a new type of “dephlogisticated air,” which was already a familiar substance and suggested running familiar tests during examination — the scientific community saw nothing special. Lavoisier on the other hand referred to the gas as “air itself entire” along with a brand new set of tests for examination. This new perspective and approach inspired the community and eventually made oxygen more widely understood.
Sir William Crookes invented the “Crookes tube” in the 1870’s to analyze streams of electrons. Overtime, he realized that with a slight modification in the voltage applied to the tube, a florescent light was created. Crookes and other scientists saw the light as “fogged photographic plates” but physicist Wilhelm Röntgen saw something much different. Röntgen thoroughly experimented with the light for seven weeks and concluded with what we now know as X-rays.
Throughout the 18th century, a number of astronomers observed a peculiar star and made notes about their observations. Twelve years later, one of those astronomers, Sir William Herschel, manufactured his own telescope to observe this star more clearly and was able to notice an unusual “apparent disk-size” characteristic. Studying its motion more carefully, he identified that it was indeed not a star at all — it was “a comet!” Several months later, he observed that its motion was actually unlike that of a cometary orbit, but more as planetary —he named it Uranus.
I hope at this point, the basic paradigm structure and process are fairly understood. At least to where we can fit our knowledge of journalism into this structure, breaking apart the phases of the profession to better understand how each developed.
For the sake of this article’s length, I’ll analyze a segment of digital journalism by outlining current and potential web paradigms listed below. I’ll break my explanations into (N) Normal Science, (T) Theories, and (A) Anomalies.
- Web 1.0 (1990s): “What are journalists writing on the internet today?”
- Web 2.0 (2001–current): “What can I write on the internet today?”
- Web 3.0 (future?): “Let me know when a story about origami paper planes is published by an expert aeronautical journalist in the Windy City.”
(N) It was understood that only journalists had the authority to publish online, and others would visit websites to read content.
(A) This understanding of the ‘web publisher’ gradually began to crumble as the internet became more widely used. The demand for freedom of speech online grew as ordinary people wanted to publish their knowledge and opinions.
(T) Theories began to form to make it easier for the public to publish: comments, chat tools, blogs, social networks.
(N) This is the current state of the web, a participatory culture where individuals are instinctively using their own websites or social network profiles to make their voices heard and engage with others.
(A) This openness has lead to an information overload. In many cases, the information we seek is bombarded with unexpected sources or irrelevant content altogether. Publishers see this as a burden of competition for reader attention and readers see this as a difficulty to find the right information when they need it.
(T) Subscription and ‘following’ helps publishers keep in touch with readers by eliminating their need to go hunt for information. Many theories continue to toss and turn within newsrooms to try to increase engagement such as frequency of publishing, exclusivity with paywalls, going ‘where the reader is,’ and focusing on eye-catching formats like video. All of these may temporarily alleviate competition but the challenges of overload and engagement are still at the forefront.
(N) Web 3.0, titled Semantic Web, suggests we prioritize digital quality. It focuses on improving the utilization of semantics for content during publishing and delivery.
Semantics are structured, machine-readable properties— otherwise known as markup or metadata.
The mission of this proposed paradigm is to use semantics to identify new opportunities for content, internal and external to the newsroom. This is a big step from the initial “shovelware” stage of publishing that lacked attention to detail and currently limits journalism.
(T) Today, many news websites already use semantics to define an article’s title, date, and keywords, but there are many more properties unutilized. Technology is able to use given semantics to fine-tune the overload of information into a filtered handful. No longer is an individual bombarded with irrelevant content but rather only content whose semantics align with their preferences — same goes for preferences of third-party services. Weather providers are using location semantics to aggregate local news on forecasts and to distribute local forecasts to Facebook event pages. Google recently released a more specific markup for events and is, therefore, able to deliver events more efficiently by location. Markup for news articles is also proving to be beneficial as seen in the recently improved personalization on Google News and New York Times.
Technical theories supporting semantics:
- Automation for identifying semantics, such as Reuter’s Open Calais and Google’s Natural Language tools. However, the reliability of accuracy is questionable.
- Artificial intelligence is a brainchild of semantics that attempts to use its properties to uncover new analysis and purposes for content. A.I. has recently gotten a lot of curious hype in journalism, yet it brings it’s own set of concerns such as filter bubbles and bias.
Newsroom theories supporting semantics:
- Explainers is a form of Explanatory Journalism that provides supportive, background information for a particular article. For example, The Washington Post’s NFL Player Cards and Knowledge Map, as well as and Bloomberg’s QuickTakes.
- Structured journalism is the piecing together of previously collected article segments. David Cohn of Circa and his team initially saw an opportunity with semantics (“atomization”), stating “when you break the story down into something smaller, you get something bigger.” However, their first iteration failed and was abandoned.
(A) Other anomalies that semantics may impact include:
- Not enough background information for an article’s topic
- Lack of trust and credibility coming from the reader
- Insufficient presentation of facts or lack of facts altogether
- Time consuming to gather facts and explanations
Will the Semantic Web succeed?
This proposed paradigm has many theories in play and is rapidly gaining acceptance. However, they all have their own challenges and hesitation from the industry. Whether these theories transform from an idea to proven fact depends if their implementations succeed and are understood — lead to profit and sustainability. For theories moving forward, the basic paradigm model recommends they must:
- Eliminate an existing anomaly
- Respect the rules (ethics) of the profession
- And, articulate accurately to avoid differences in perception
I hope this article was useful for you. I encourage you to read The Structure of Scientific Revolutions and would love to hear your interpretations of the paradigm structure. Thank you Mr. Kuhn!