Thanks, all, for expanding and advancing the discussion in this thread! I am learning much. My thinking is definitely shifting, though to what, I’m not sure.
I will try to add to the major themes I’ve picked up.
Solutions to metadata note capture and their problems
Both @Eugleo and @ja_rule point out the tension—paradox, really—in maintaining “good metadata” without falling into the trap of predictive organizing.
I think solutions to this tension fall into four categories:
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Go for volume. When writing notes, add as many kinds of metadata as you can think of in the form of tags, key phrases, and so on.
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Automation: your fAIry godmother. Use scripting and other automation tools to add contextual information based on any available cues. E.g., you may use a script to add a project-specific tag to any note created while a Toggl timer is running, or you could detect any note that mentions “note-taking” and “knowledge management” in close proximity and add a “PKM” tag.
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Don’t sweat the small stuff. Assume that your notes will contain relevant metadata based on the fact that they’re actually about something, and that when you need it you’ll be able to search for it.
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Moments Notes snap together like magnets. The metadata isn’t in or attached to the notes themselves—it’s in the relationships between the notes. Use linking and structural notes to develop themes and clusters. This is essentially grounded theory for your notes.
(1)’s challenges have been discussed at length above. The practice of adding a bunch of speculative tags or key phrases to a note as you write it is really just a sneaky kind of predictive organizing.
(2) has both a shallow and a deep manifestation.
Shallow: construct Rube-Goldberg machines of triggers and actions to enrich your notes with automatically-added metadata. While this seems great, arguably you are just doing predictive organizing behind the veil of automation. You still have to make predictions about what kinds of conditions will add useful context later on.
The deep implementation is essentially waiting for a fairy to solve your organizing problems. There are some neat innovations taking root in this space (e.g., use graph analytics to algorithmically identify important relationships in data, use machine learning to identify key features of data). However, in my opinion, these are way off from applicability (especially in the general purpose sense). They’re also way over my head without a big cup of coffee and a few interrupted hours spent understanding them.
(3) is effectively Tiago Forte’s position. Your notes already contain the metadata in the content. Organize according to non-content rules and use your computer’s search capabilities to find what you need, when you need it. A key problem with this approach, though, is that it struggles with scale. Even though I have great search tools, it still takes me far too long to find key ideas in 10,000+ PDFs.
(4) involves processing, and is the most hands-on of these solutions—though, as smarter people than I have argued, “processing is the work.” At a basic level, this is the approach Luhmann took, and it is the root idea of the compositions @masonlr mentions. It is also evident in @nickmilo’s structural Map of Contents notes and other tools. However, if you don’t have time at the front of the workflow, you won’t do this—thus it requires a building of habit.
Further thinking on purpose-based note organizing strategies
Earlier I advocated for pushing as much organizing as possible to the use-case—that is, try to embed information in your notes, and then come up with effective ways of finding and using that information based on what you need it for.
In turn, I suggested that there were probably “design patterns” in note use, such as using a pseudo-tag with tasks you want to review later.
A paradox is that in order to implement these kinds of patterns, there is an implication that you need some anchoring metadata in the note data. In other words, you do need to kinda predict the use when you’re writing it—else you wouldn’t add the pseudo-tag.
Here, I think it may be useful to delineate the different kinds of purposes. @ja_rule mentions “augmented memory” and “aspirational thinking” (I have been calling the latter “augmented creativity,” as it happens). These might be useful categories in figuring out design patterns and recognizing when to use “anchor” metadata.
I’ll first focus on aspirational thinking. I think this kind of use-case is best suited for purpose-based organizing patterns like Maps of Content. In other words, you should never try to predict aspirational thinking organizing needs.
The former is, as @ja_rule put it, mundane—and, I think, the most insidious. It’s the mundane that most depends on our at-time-of-capture good metadata. This is a nice insight, because it means you can probably relax about metadata unless it’s something obvious, like a task or a person.
So, if this rings true, it means that good metadata depends on whether the information we’re looking to organize is of a mundane or aspirational purpose. If it’s the former, use organizing design patterns that make sure you have a standard anchor to that data over your database (e.g., a task pseudo-tag). If it’s the latter, use organizing design patterns like note linking and Maps of Content to make sure you can trace back to the idea based on some creative need.
