principle: homophilly -> clustering -> reification -> vocabulary extension
- Dominant language
- Python
- Stars
- 21
- Forks
- 2
- Avg merge
- 6h 33m
- Merged PRs (30d)
- 91
Description
formalize the the dynamics leading up to the phase transition of learning a new concept. the idea here is to motivate developing mechanisms that contain affordances for organically facilitating this pipeline's action. examples (of desired outcomes) include:
* the 'invariant' lineage linting rule that suggests the vocabulary merits extension (or some chain members are missing a tag)
* partitioning views by tag (to constrain the readers' attention to clusters)
* ...
Contributor guide
Research direction
Start by translating the homophily → clustering → reification → vocabulary-extension pipeline into explicit requirements, then inspect the existing invariant linting, tag-based views, and decision-record reference tracking described in the issue. Done should include a decided mechanism for motivating vocabulary extension and a clear definition of the desired linting and clustering outcomes.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- tooling
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100