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Post-open-source software (POSS) is a term used in discussions of source code published without an explicit software license. The expression gained attention in 2012 after analyst James Governor described some younger developers as treating licensing and governance as unimportant when committing code to GitHub. Richard Fontana later used POSS more…
The analysis highlights History and Overview as prominent areas in the source structure around Post open source.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
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See recurring relationship patterns around Post open source before inspecting the individual extracted relationships.
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poss used code explicit licensing license governor developers github software without 2012 analyst james younger governance committing fontana wrote post-open-source
TTTA extracted structured relationships around Post open source. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
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The concept neighborhoods around Post open source bring nearby vocabulary together. In this analysis, examples include Term, Without and Used. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Post open source, one of the stronger structural bridges in this analysis connects Post open source with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Post open source to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History & Overview, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Post open source · EN edition · Analysis: TopicsToTalkAbout