Information Science: Working with Information, Part 2 – Information Gathering

Information Processing Algorithm

If we greatly simplify the algorithm for working with information, it looks like this: gather → understand → transmit. This short version helps clarify the main tasks when dealing with information.

Of course, the algorithm can be expanded to include more detailed steps for high-quality, thoughtful information work. The extended version includes:

1. Receive information
2. Assess and understand its value
3. Integrate it into relevant connections
4. Double-check it
5. Accurately describe the results
6. Discuss the findings
7. Prepare the final version of the research

This process can be visualized as a conceptual flowchart of operations.

General Description of the Information Science Method

The method of knowledge in information science can be presented as an action algorithm (info-studying):

• GATHER:
  • “State acceptance” of incoming informational elements (abbreviated as inels) and initial assessment of each inel for probable reliability
  • Assessing the probable reliability of the information source
  • Creating meaningful “collections” of evaluated inels relevant to the research topic
• UNDERSTAND:
  • Working with evaluated information through mental experiments: grouping, combining, expanding, and condensing using various filters
  • Translating data from one form to another (verbal/numerical to visual and vice versa)
  • Forming your own conceptual framework for the studied period to interpret events accurately, considering communication barriers like era, ethnicity, and other factors
  • Outlining probable scenarios for the development of events (life of an object or person)
• TRANSMIT:
  • Discussing research results and external peer review to improve overall quality
  • Forming an image and model of understanding of the historical event
  • Creating presentation data sets and research conclusions in a format that is easy to verify and engaging

ADDITIONAL PLUS: Developing self-organization skills in research and supporting a culture of sharing only verified data.

Overall, this algorithm aligns with current scientific standards. Its distinction lies in how a person prepares information (steps 1–3) and how they verify it (step 4). There’s also an emphasis on creating a clear, verifiable context (step 5), i.e., how research results are presented to an audience. This last stage is crucial: a discovery presented carelessly or unclearly may go unnoticed or misunderstood. At best, the researcher is forgotten; at worst, they’re discouraged from further study.

Gathering Information

The first task in working with information is gathering and selecting information. Essentially, this is about compiling an “anamnesis” of the topic, allowing for an initial classification: what is present and what is missing in the collected material. It’s like creating your own imaginary museum of facts based on informational traces left over time.

Sometimes, gathering information may seem trivial or even automatable (e.g., with bots). But this simplicity is deceptive. We always face incomplete knowledge about an object, due to our limited understanding of the world and the abundance of unverifiable information (fabricated, lost over time, locked in closed sources, etc.). Automated operations only increase the volume of information, as seen with the World Wide Web. But more information doesn’t mean it’s useful or that it will help solve practical problems efficiently. Modern information systems focus on quantity, not quality or verifiability. As the number of information fragments grows, so does the impossibility of adequately familiarizing oneself with them.

Of course, not all information is false; some reflects the probable real world. But this reliable information is like a needle in a haystack—hard to find and sometimes impossible. That’s why detail and clarification are so important, achieved through careful material collection.

Thus, gathering and selecting information is a key part of quality research. The method of questioning helps reveal what information is available, what’s missing, and what’s absent but important for understanding the research object. Selection is based not only on the information itself but also on analyzing its source.

The questioning method helps logically prepare multi-perspective information sets for research, which involves a kind of “state acceptance”:

• Receiving information: Recording informational elements (inels) and their sources
• Assessing probable reliability: Preliminary evaluation of the quality of the information and its source
• Forming meaningful collections of inels: Creating a database of elements connected thematically, chronologically, or systematically

These are the first three steps of the information processing algorithm. The point is simple: research should be based on well-collected, selected, and preliminarily labeled (evaluated) information. This isn’t new, but the illusion of knowledge and deep understanding often leads people to skip these steps. Education systems often reinforce these illusions, and new forms of knowledge assessment, like testing, make them automatic. Modern education doesn’t encourage deep understanding—it encourages knowing “something” (including false postulates). As false knowledge accumulates, true understanding becomes more difficult. Relying on a false database, a person can’t reach a true understanding of things or reality, leading to cognitive helplessness. Society drowns in information ignorance, and social ignorance prevails.

The main tasks at this stage are:

• Gaining a clear understanding of the limitations of verifiable information on the topic and its context, based on personal experience, not others’ claims
• Understanding the depth of ignorance about the topic and related objects
• Developing skills in gathering and selecting information, considering the probable reliability of the information and its source
• Creating a more detailed and systematic information base on the subject

In a way, this is the first stage of acquiring “holographic knowledge”—multi-perspective recording of information about an object, allowing for different paths to similar understanding.

Thus, information science approaches teach us to perceive information differently: not automatically, but thoughtfully and consciously. This improves information quality through detail and systematic clarification.

Often, research ends at this stage or drags on for years, turning into detective-like searches for inel collections. Sometimes, it becomes simple collecting of artifacts and information about them, forming personal imaginary or real museums.

Practical Application of the First Stage Algorithm

The first and most essential skill in information science is the habit of “meeting” an inel about the research object through a series of questions. These questions allow you to “name” any piece of information, i.e., clearly answer WHAT (or WHO) it is. Then, the inel is linked by place and time—answering WHERE and WHEN this WHAT (or WHO) happened (lived, occurred, existed). Every informational element should have its “home port”—a source that lets you determine WHERE it came from. Further data structuring is done through questions like HOW and HOW MUCH, as well as complex “sieve” questions (see the reference chapter “Additional Features of the Anti-Virus”). These complex questions help clarify the specifics of ongoing changes.

Systematizing facts and organizing them into an information dossier disciplines thinking and prepares the ground for quality conclusions. A brain trained in classification and systematization starts to automatically notice “needed” or “missing” data for this information processing model. Six months of practice is enough. Complex “sieve” questions provide a wide range of clarifying, specific additions to understanding the research objects.

The main tasks of the initial information processing algorithm are:

• Developing the habit of seeing the object in context, as part of the overall system of reality
• Ability to identify information gaps and ask the right research questions
• Evaluating information for probable reliability
• Evaluating the information source
• Forming collections of inels

Each question, in a specific situational context, will have its own special group of descriptions. It’s impossible to offer a fully universal algorithm for every research object, but a general algorithmic research scheme can help ensure important questions aren’t missed at the first stage. Depending on the research specifics, each significant aspect can be further explored. Research should include a variety of possible facts affecting the object’s history, as well as their systemic interrelations.

Evaluating Sources

Source Studies. Sources are usually divided into mass and unique. Mass sources include the wide variety of books, textbooks, reference materials, and the press. Note: before the printing press, mass literature was rare. Books were often read publicly (sermons, lessons, etc.), and there was an entire industry of copying books. It’s said that hundreds of thousands of monks were involved, and each student had to copy their course materials. Thus, all books before the printing press can be considered unique. Even if a book was copied many times, only a limited number of copies have survived, scattered around the world. Unique sources are usually collected by archives and private or state collectors of antiquities.

Mass sources, in turn, exist in print (usually paper) and digital form. The possibilities for falsifying digital sources are limitless. Even in the 19th century, scholars (like Sensibos and others) noted that a text is nothing like the actual past event it describes. The author’s psychological experience and language influence the text.

The separate science of paleography studies books—the language features of a certain period, the time of the text’s origin, and the author’s interpretation. Sources are also studied as objects of material culture (medium, method of writing, design features, etc.).

Other informational elements (see the example of an inel “sales receipt”) and objects of material culture can also serve as sources.

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Essentially, the first stage of the information processing algorithm helps the brain “get used to” thinking in a new way: “once a skill, even a complex one, is mastered, it requires less and less cortical activity.” This algorithm allows you to collect material that is dynamic, changeable, variable, and constantly updated. Such material requires an unconventional approach, like fuzzy logic. Fuzzy logic doesn’t use “yes/no” (0/1) positions but offers a whole spectrum of probability degrees. When working with information, we always deal with probable descriptions of events. These descriptions lack black-and-white assessments; instead, there’s a palette of gray shades and nuances.

Additionally, the brain should start enjoying the process of understanding. The brain gets pleasure when it can predict the possible outcome of its research, conclusions, and forecasts—that is, the result of its understanding of reality. For example, if you have doubts about the number of Napoleon’s troops crossing the Neman River, based on conflicting data about HOW MANY, you start studying the topic and come across a letter from Napoleon to Emperor Alexander, written from Moscow, beginning with “Sire, my brother!” and ending with “A simple note from you, before or after the last battle, would have stopped my advance, and to please you, I would have given up the advantage of entering Moscow. If you, Your Majesty, still feel as you once did toward me, you will kindly read this letter.” You start thinking and ask yourself Kant’s question: “What can I know?” You recall Napoleon’s remark about measures and his actions to introduce a new system of measures in 1812, aligned with the metric system. Interestingly, in 1812, Paris adopted a new system of measures as a compromise between the metric and traditional systems. At the same time, the “Depot of Maps” was renamed the “Military Topographic Depot.” War is a convenient time for experiments with measurements and, of course, maps! At some level, it becomes clear that not everything is as presented in the official version of the 1812 war, and the events of that period are far from straightforward. Kant’s question is no longer idle: we don’t understand or know many things. Incidentally, most data on the 1812 war in archives dates to 1912, the war’s centenary. So, a hundred years later… If you compare perceptions of WWII 50 years ago, 30 years ago, and today, it’s clear we’re talking about entirely different things. Looking at the war through a regional lens, it’s obvious that in 30 years, it will be very hard to figure out what really happened: huge amounts of new material are created, and old, archival materials are destroyed. Maybe it will simply be impossible to understand what happened in 1941, especially if certain interested parties make an effort.

To be continued in the next “Information Science” article.