Discourse between analysis, synthesis, and observation

Narrated by Anchor.fm AI

I re-encountered the concept or study of discourse analysis recently. I have heard of it before and found it intriguing but not so much to actually study it. I did a quick search on the topic and found it applied to a wild range of what might by described as a discourse. At the end I find more interesting, it is the study of specific communications between specific people or on a specific topic. This study dissects the actual words, spoken or written, and evaluates the precise word choices, the timing and pacing, and interjections. It would also compare these to some expectation of what the person normally would or would not say, and what generally is being discussed at the time. Discourse analysis interprets what is actually said or written against a broader context. I like to think of this as the old common sense notion of reading between the lines, but with more rigor.

The academic concept is broadened to cover larger topics involving a multitude of discussions such as when applied to history, or even a record of human activity such when applied to sociology. The study considers human activity as a form of discourse especially when that activity encounters conflicts. Instead of responding with words, people modulate their activity either to be more confrontational or to be more accommodating.

From my under informed perspective, I see a lot of parallels with body language interpreters. They match the words spoken with the body gestures and then decide what gestures are significant to the discussion and what they may say. Given the broad nature of the description of discourse analysis, it certainly includes body language.

For this post, I am thinking about the narrow interpretation of studying very specific communications between very specific people in a very specific context.

Again, I largely forgot there was an actual academic term for the study. I last heard of it a long time ago and at the time only thought of it as academic discipline. I make the analogy of the difference between how I would read the Bible and how the Biblical scholars study it. I do interpret what I read, in a sense reading between the lines, to get a sense of what may be really happening. I do not approach it academically or enter into debates about it.

Discourse analysis is about opening the interpretation to debate with someone actively defending some interpretation against competing interpretations. If you are not going to debate about how you come up with some interpretation, you are not doing discourse analysis. Instead, you are doing discourse.

Discourse analysis is making explicit what probably always occurs implicitly. When we exchange words, especially about a topic with high stakes and with competing ideas, we pay close attention not only to what is said, but who is saying it, when they say it, and in what context they said it. I recall many group discussions where someone would finally speak up long into a discussion to object to something and from the tone I could tell he objected from the very beginning but merely kept quiet. In one instance I can think of, he may have been hoping that others would eventually come to his conclusion without him saying anything, but had to interject when the group consensus was converging to an opposing conclusion. My point is that I was analyzing the discourse in that I heard a lot more being said than what actually was said. Looking or even listening to the actual words may give the impression that he was being kind and helpful, but given the context he was irritated and perhaps even disgusted.

Discourse analysis occurs normally as part of discourse, when the topic is important enough. It does not always occur, however. I think of the counter case of two people in a heated argument involving raised voices and aggressive gestures. They may be responding to the body language or the context of the words being said, but in this circumstance there isn’t enough time to pause and more carefully consider the context.

It is my experience that discourse analysis occurs most naturally during meetings or conference calls, or more recently with video conferences where everyone has their cameras on. In the conference, only one person is talking, and sometimes he is only addressing one person. Meanwhile, everyone else is listening or patiently waiting for the discussion to address them again. Some listeners may be paying close attention to this side topic that does not apply directly to them and try to interpret what is going on. They may also be checking out the expressions of the other listeners.

Even when the topic was so specifically between just two people so that it should have been moved to a private room, there is information being exposed to the broader audience. Obviously, we can learn something about the working relationship between the two people. But more subtly we can learn that we didn’t realize was important prior to that meeting. That lesson may not directly apply to our jobs, but it could help to understand why things are the way they are. Very frequently, the answer is that it has to do with the specific personalities involved.

I am describing active listening and emotional intelligence. This is not an academic discipline. It is just a matter of being a skilled and alert listener. The mechanics are very similar to those used in academic discourse analysis. The difference is that in normal active listening we keep our reasoning private. Our reasoning instead gets exposed by how we respond.

Another distinguishing factor is that whether you are an active participant in the conversation. If this is a conversation where you still have an opportunity to interject and possibly persuade others, then we are active listening with emotional intelligence. If the conversation is over and there is no further opportunity to change the outcome, then we engage in discourse analysis.

In much earlier posts, I described my distinction of present tense science and historical science. In present tense science, you or your experiment has the opportunity to change what will happen. In historical science, we instead have to figure out what happened by the surviving records of what happened. Often, even in the modern age of big data, much of the most critical information is lost.

I also used this to distinguish the actual practice of science from data: science is about experiments in the present, while data is about what happened in the past. There is a tremendous luxury to be in present-tense science where you have control over what data to collect or to re-run experiments to catch something you missed. In contrast, data is about navigating through a morass of forever missing data.

Deep data analysis is very similar to discourse analysis. Both are trying to retrieve information that was not preserved. Both attempt to read between the lines, but data analysis applied to big data is able to reconstruct a much broader context for the data. Machine learning or artificial intelligence is an automated version of discourse analysis, but it may be more similar to what I described as active listening and emotional intelligence, because machine learning rarely explains how it came up with a conclusion.

All of the above is to grant myself a little authority to apply discourse analysis.

There is a discourse within what I call science. Science is another term that has broadened to a wide range of disciplines. I am specifically discussing science as it relates to human engineering. This science is something that can be used to create things that reliably behave as predicted. It is typical of mechanical or electrical systems, but it is also increasingly applicable to biology. This science has success in explaining behaviors of existing systems whether man-made or not. Applying this science has success in creating new things that behave as designed.

Within this category of science there is that distinction between analysis and synthesis. Analysis attempts to describe behaviors or qualities something that exists. Synthesis attempts to create something new with a desired quality or behavior. There is a conflict between the two, especially when analysis is applied to something that came from synthesis. This can set up of an argument that plays out through iterative designs. This argument becomes explicit in the practice called Agile that answers its presumption of not knowing everything with its short sprints and minimum viable products.

There is a perpetual argument between analysis and synthesis. This argument uses natural objects and human artifacts instead of words, but it plays out similar to a discussion. We can study this discussion using something similar to discourse analysis, but primarily in hindsight when studying what actually happened. However, because it is an argument between two entities, there is not much opportunity to apply discourse analysis or even active listening to the current projects. The arguments are too heated in the sense that there are deadlines to meet whether to produce something new, or to advise a course of action for something already existing.

In analogy to my earlier discussion, the opportunity for active listening or private discourse analysis comes when there is a third party audience. The third party in science exists in the form of the observation science. The examples I keep using are that of the naturalist and the astronomer. My impression is that both are keen on careful collection and recording of objective observations. They may rely on technologies to do this. They may also use other analyses to guide what they should look for. Their focus though is neither analysis nor synthesis. They are focused on collecting observations. These are the active listeners of the sciences.

I think the naturalist is the better example because they observe something that is responding to human activity. They may know the theory about what should happen in response to different types of human interventions, sometimes careless, and sometimes deliberately trying to be helpful. They report on what they observe and in that report try to reconcile their observations with the theoretical or intentional predictions.

Like the active listener in a conference call, the observation scientist will be silent while analysis and synthesis have their argument. They will speak up when the opportunity comes up when the others are receptive to seeing the data. Then he will go silent again as analysis and synthesis argue over what the data really means.

For this post, I introduce the observation element to create a third party in the conversation. The observation collector is not going to directly change the course of any action. The analysts will massage the data to build their recommendation for what actions to do. Similarly, the synthesists will adapt their design to adapt to the data. The observationists recursively applies his discipline to the observe the discussion.

I liken myself to an observationist. I described it earlier to distinguish what I do with data from what is commonly described as data science. I described myself as an naturalist of the datum, or a dedomenologist.

I entered my career aspiring to the a synthesist. I fantasized about making new things, and accumulating a lot of patents for original ideas. Once in college, I was more drawn to analysis. I think I exaggerated the importance of mastering analysis in order to do synthesis. Innovations often come up with something that challenges the analysts to explain. In any event, I began to prefer analysis.

I was especially attracted to the mathematical element of analysis. First I would admire the formula that summarized someone else’s analysis. Then I delighted in learning the mathematics to fully appreciate the formula or apply it for myself. I would say that I spent most of my early career in the analysis, although at times I did have the opportunity to participate in designing something new.

Eventually, I dropped out of both and devoted my time to observing. This coincided with the explosive growth of data technologies making it possible to get paid to study data without having to show work as lines of code written or experiments completed. The money question for me was to collect relevant information and to challenge their varacity.

In this site, I often discuss my distrust of what I call dark data. As an observationalist, I prefer direct objective observations from trusted sensors over anything derived from theory. I describe this as a conflict between science and observation. Observations challenges the results of both analysis and synthesis. I go further in saying that observations challenge the authority of both. With enough observations, there may come a time when we no longer need analysis or synthesis as customarily practiced. We can end up making decisions based on data alone, and applying an expiration data for those decisions out of an acceptance of the tentativeness of the conclusion. I describe this in context of government I call dedomenocracy.

My observation about the sciences is that they are increasingly influenced by government. Government funds a lot of analysis and synthesis. This comes in the form of grants to research institutions or contracts for performing government work. In addition, many scientists work directly with government. All of this started as a good thing. I recall early on supporting that government should have a healthy budget to fund science for full range between grants for pure theoretical science and contracts to build things for government or things that only the government would fund. A part of that support came from the hope that I might myself benefit from the funding.

Applying a discourse analysis to the broader context of government influence over science, I observe a growing conformity of science to serve the needs of government. This is not entirely deliberate.

Government funding for analysis depends on that analysis providing something for government to consider. The awardees offered a promising proposal that can producing results that would further the objectives of the government. Frequently the most rewarded analyses are those that make the strongest case for a bad consequence if nothing is done. I noticed the trend a long time but now it is commonplace to see a government-funded analysis being reported as something was discovered that could have a very bad outcome. The headlines have two parts: first describing a confirmed observation, and then describing how that could lead to catastrophe of the largest scale that is reasonably believable.

The opposite incentive exists for government contracts for synthesis. Here the reward is for something that will benefit everyone. Even if the project is under a lot of distress, we will hear reports that the effort will someday be rewarded with some great outcome everyone would appreciate.

Government funding of science aligns both analysis and synthesis with the government’s goal to continue to grow. Analysis tells government that bad things will happen if it does not intervene. Synthesis offers something for government to do that will benefit everyone. My point is that the funding element discourages the contrary findings. Certainly, government is not interested in projects that admit that they are unable to succeed in their goals. Likewise, government had little incentive to fund analysis that would find that there is nothing worry about.

Government funds analysis to give government something new to worry about, or some reassurance that it should continue to worry about something old. Government funds synthesis to show the electorate that they are working on something that will eventually benefit lots of people, just not yet.

Our current enlightenment based government cannot run out of thing to pursue that would resolve some worry. Ideally these governments needs an ever increasing list of things to worry about and to resolve through new contracts. Both types of science are funded to fill that need.

The recent COVID19 experience illustrates this very well. In the initial phase, government funded analysis provided the justification to be very worried about a new virus by providing the potential catastrophic worst case of everyone being susceptible and each has a high likelihood of dying. At the time, there was non-government funded science that showed that this was not happening or was very unlikely. The more fear-invoking analysis won out and the population rejected the other findings as deliberate misinformation.

Drastic policies were put in place to do something that would allay the fears. The initial policy was described as flatten the curve to stretch out the inevitable deaths over a longer period of time so as to not overwhelm the medical systems. This was in some sense a synthesis and there were nice marketing to illustrate the future promise of the approach. Almost immediately, there was evidence that the medical systems were getting under-utilized overall so that some hospitals had to shut down operations or seek special funding. This information was suppressed. This information did not come from government funded science.

The larger synthesis project concerned the funding of multiple vaccine trials with the expectation that only one would succeed. It turned out that most passed the emergency-use test. This was followed by the synthesis project to distribute the vaccine. This is ongoing. The government gets to claim the success in the absolute numbers of vaccinated people as if vaccination itself were the benefit. Meanwhile, non-government funded science is reporting that the vaccines do not prevent infections or spread. The vaccines only reduce the symptoms, but this can have the contrary result of allowing the infected to become super-spreaders especially of some new more deadly variant. This information is suppressed because it is not coming from government funded science.

This vaccine program like all vaccine programs come with the risk of harming people, even people who were not at risk of the disease being vaccinated against. In this case, there are reports of vaccine injury and death of young people who are least at risk of the virus. Government dismisses this result as either exaggerated or a necessary loss for a greater good.

The greater good calculation has two parts. The first part was expressed very early on that all of the inconveniences imposed by government was justified it would save a single life from this singular type of virus. The second part was expressed later on that said that deaths and injuries from the vaccines need to be tolerated.

This occurs in most other government projects. Government action is required to prevent any possible bad thing that might happen from natural causes or from human activity outside of government. Everyone must accept the occasional bad things that happen from government action because there will be some future benefit and the government’s intentions to prevent other bad things from happening.

While this government activity is occurring, there are outside observers and scientists who try to point out that the government is exaggerating the extent and likelihood of the bad things from happening, and that the government is exaggerating the potential benefit of the pursued solution.

There are many examples within the defense department where lots of money is spent building weapons against hypothetical threats that are exaggerated. Many of these weapons or subsystems fail to do what they promised but they remain on the program indefinitely.

The important thing is to keep the government in the business of solving problems it discovers. Government funded science is serving this need by providing the new problems to solve and the new solutions that would solve them. Meanwhile, there exists other science by concerned scientists who are not funded directly by government. If this outside science contradicts the government, then we dismiss that science as misinformation because it did not come from government.

The extensive funding of science by government has degraded the value we once received from independent science. That value was producing results that would reassure us that government investment is not needed, or that the government’s proposed solutions are not or will never work as promised. This type of information is no longer permitted.

When government funds both the acceptable analysis and the accepted synthesis, there ceases to be a discourse between analysis and synthesis. The third party observation science has no conflict to observe between the two. The analysis fully supports the need for the response, and the response fully answers the analysis. Given the need for continued funding, the analysis will not report that the response will not work, and the synthesis certainly would not challenge the analysis the supported the necessity of the response.

I will wrap up by returning to the first topic of discourse analysis. It occurs to me that I arrange these blog posts with the main content at the bottom. Above my main point is an extensive description of the context of my thinking. In this sense, my posts are pre-emptive discourse analysis, I spend more time describing the context than I do the main content.

2 thoughts on “Discourse between analysis, synthesis, and observation

  1. Pingback: Follow the neurotic | Hypothesis Discovery

  2. Pingback: Discourse Analysis of a Diary or Blog | Hypothesis Discovery

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