Buzzard Game 04 CJ Content Analysis
by: Dave Gorum
Buzzard Game 04 CJ Content Analysis
Metadata:
- Title
- should this be headless? pros / cons
- With
- near (how to manage this trust?)
- relationship stance/dynamic(?): co-author, type of thought collaboration?
- Time composed:
- when(s)
- ^ model that makes room for multiple sessions
- Location composed:
- where(s)
- ^ model that measures geographic flow
- Media composed in:
- surface(s)
- ^ phone, paper, tablet, voice recorder, app? discuss
- Media Type
- type (see: media primitives)
- purpose/intention
- ^ this has something to do with an evolution of complexity beyond just file type. so we have jpegs and mpegs and they’re more or less generic media containers. what about an intentional layer to the metadata. need examples of purpose. (idea, scribble, sketch, freehand)
- Link(s) / References
- internal / external source
- ^ how do these relate to Navigation? how to think about references?
- Game Relationship (see: game metadata)
- Access Privileges
- ^ (part of discussion of trust-driven access bubbles*)
- Navigation
- ^ these are manually or algorithmically intuited flows. backlinks, links, other connection types? signposting. I’m my thinking is heavily weighted toward wayfinding. what other ways to think about navigational metadata?
- Media artifacts
- blocks (see: blocks standard)
Additional docs (that don’t exist but could!)
“media primitives” would define the minimum standards for thinking about how to ingest media file data. this data will increasingly come straight from sensors? (I might have primitive thinking around this)
“game metadata” would define the relational metadata for whatever game session produced the artifact
“Blocks standard” would contain the different types of block primitives and their information membrane (ie metadata and ??)
Process notes
Heavy / Light range? (thinking here of one of CJs consumption posts [media, note, link] vs a beefier post. whatever this standard is should work across that gradient)
When modeling keep in mind versioning. I tend to think a few steps ahead. Working outside in.
the session should be able to ask you simple context questions to help with future querying. collection as part of a check in / out process. methods of collection suitable to personality type and method of work. clean data is more efficient fuel.
Thinking of all of this info as useful for pattern / thought type analysis by a machine intelligence partner. Complex pattern matching by the machine and training by the human. So, what’s the basic machine readable but with minimal context input. Maximal clear context.
handwriting training for tablet input. contextual transcoding. input standardization. based on alphabetical system. must be designed with emergent system in collaboration.
the concept of a proposal of standard. studying how eth does this or finding people interested in the study of and enactment of governance
*the trust bubbles. access based on relationship development. moving outward to public(?), broader audience (?), culture observation(?). not sure about my instincts in this area.