As I said in my last post, my reading workflow consists of GTD-like phases:Â collect, process and write.
While I wrote about collecting before, this post is about the three phases of processing notes. In the last section you’ll find a few example Zettels I wrote.
Phase 1: Gather Notes
Phase 2: Form clusters and create overviews
It has proven useful to not just dive in and convert note by note from paper to text file but prepare some kind of plan instead. Ask yourself:Â What was your reading intent and how can you capture it best?
Overviews you create yourself enable you to grasp a book’s architecture. Sometimes I’d sketch rough Mind Maps to get a feel for the content as a whole before I can start making sense of the notes. Especially if it took weeks to finish a book I benefit from creating visual overviews first.
I read the Kanban book in a single sitting, though, so the concepts were still fresh on my mind. I didn’t need an overview in this case and found it was fairly easy to cluster the notes. That’s an indicator of a well-written book with a clear focus in my opinion.
Clusters don’t lean onto the book’s outline. A book’s index for example collects references, not caring about the table of contents or the flow of ideas. For definitions of terms a similar approach is useful: collect usage examples in the text and definitions themselves to get a clear picture of the term’s meaning. Clusters can be topic-based, too, just like an index.
This is what I call ‘orthogonal to the content’: they don’t adhere to the succession of pages and sections. Instead, clusters form themselves around any purpose you deem fitting.
I basically repeat this subroutine:
Start with a note as general as possible per cluster.
Get to the details in succeeding notes when needed. Atomicity of concepts is key here.
Feed everything you found out during step 2 back into the overview of step 1. Be as concise as possible.
At first I will try to grasp a whole cluster in a single note. This is especially useful when I can abstract away from the details and carve out a clear concept.
Second, I find out if a cluster’s main point has too many prerequisites to stand alone. It might be a conclusion which draws from lots of assumptions or from complex models I’d need to explain. I prepare the conclusion first and then branch off into other notes to capture all the necessary ideas. This is where links come in handy: the details point back to the concept note and the concept note mentions its detail branches.
The underlying principle I’d call the principle of atomicity: put things which belong together in a Zettel, but try to separate concerns from one another.(2) For example, I might collect a list of assumptions in one Zettel which serves as an overview. like hard determinism. A related argument and its conclusion will be kept in another Zettel. Moral responsibility under hard determinism is a good example. I can re-use the arguments without buying into the assumptions because the arguments are of sufficiently general form. Atomicity fosters re-use which in turn multiplies the amount of connections in the network of Zettels.