Milieu specific characteristics from 10 Zettelkasten user groups

Edmund

My research leads me to a nice model with like-minded groups of people working with Zettelkasten method. It's a visual concept that helps me to find further insights to understand Zettelkasten interactions. My initial view is showing groups with different learning approaches and different knowledge intensity.

The largest group according to my survey are the "Self-Learners and Autodidacts (29%)" with some milieu-specific characteristics:

• Personalized learning journeys driven by individual interests and goals.
• Flexibility to explore diverse topics and resources at their own pace.
• Utilization of Zettelkasten as a tool for self-reflection, goal tracking, and continuous improvement.

Even though we all use the Zettelkasten method, each group still has different needs and preferences. This helps me better understand some of our discussions here in the forum.

Reference

• Data collected from user survey: https://forum.zettelkasten.de/discussion/2848/survey-for-template-users

GeoEng51

@Edmund This is helpful to visualize the different ways people use their ZK.

I have one question - what do you actually mean by "knowledge intensity"?

Edmund

@GeoEng51 said:
I have one question - what do you actually mean by "knowledge intensity"?

Thank you for asking. Here is my definition of “Knowledge Intensity” with three refinements:

• Low Intensity (1): Groups dealing with broad or basic knowledge, such as General Knowledge Collectors and Language Learners.
• Moderate Intensity (2): Groups engaging with specialized but not highly technical knowledge, such as Students and Academics and Educators and Trainers.
• High Intensity (3): Groups dealing with highly specialized or technical knowledge, such as Researchers and Scholars, Creative Professionals, Knowledge Workers and Professionals, and Data Scientists and Analysts.

Sascha

At least, I'd put the data scientist at the topic. From all of these groups they have the most technical type of knowledge to deal with.

Edmund

@Sascha said:
At least, I'd put the data scientist at the topic. From all of these groups they have the most technical type of knowledge to deal with.

Thank you for kidding. And our survey shows that "data scientist" is obviously not the most relevant milieu of Zettelkasten users. A model update with 9 milieus will be a solution. Or do you miss an important group?

Andy

@Edmund said:

@Sascha said:
At least, I'd put the data scientist at the topic. From all of these groups they have the most technical type of knowledge to deal with.

Thank you for kidding. And our survey shows that "data scientist" is obviously not the most relevant milieu of Zettelkasten users. A model update with 9 milieus will be a solution. Or do you miss an important group?

I don't think Sascha was kidding; he meant to write "at the top". It seems true enough to me.

Also, I don't think an n=9 survey shows much. I didn't take the survey; I thought it was only about Obsidian templates.

Edmund

@Andy said:
Also, I don't think an n=9 survey shows much. I didn't take the survey; I thought it was only about Obsidian templates.

We now have an update from n=116 (all communities):

Andy

@Edmund said:

We now have an update from n=116 (all communities):

Which communities?

Sascha

@Edmund said:

@Sascha said:
At least, I'd put the data scientist at the topic. From all of these groups they have the most technical type of knowledge to deal with.

Thank you for kidding. And our survey shows that "data scientist" is obviously not the most relevant milieu of Zettelkasten users. A model update with 9 milieus will be a solution. Or do you miss an important group?

Ah. Andy was right. From all the groups, data scientists deal with the most abstract and technical knowledge.

Edmund

@Edmund said:

We now have an update from n=116 (all communities):

@Andy said: Which communities?

Here is an overview:

But your vote is still missing

Edmund

@Sascha said:
Ah. Andy was right. From all the groups, data scientists deal with the most abstract and technical knowledge.

Yes, as a data scientist I can see, there will always be a difference between self-image and public image.