NotesNicolas: Difference between revisions
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=== Historical Context group === | === Historical Context group === | ||
Track main phrases that changes across commits. But what exactly is changing? Can the [http://en.wikipedia.org/wiki/Markow_algorithm Markow algorithm] help? It could give statistical information on changes. | |||
Interesting to detect high frequency of changes. How to trace the related changes in law? How to analyze what changed? Word frequency? | |||
Proposals: | |||
* Evolution in size of the text of the TOS's | |||
* Evolution of tag clouds over time: the top ten words across commits. Compare tag clouds. | |||
=== Simple Stats group === | === Simple Stats group === | ||
=== Timeline group === | === Timeline group === | ||
Revision as of 14:24, 16 March 2011
Group reports
Game group
Use Goodiff content in text-based narrative game. Game arbitrarily gives permission or denies to a character, like the TOS's. Use the actions semantics (examine, take, E, W, S, N) and confront them to TOS's.
Examples: you cannot pick up the TOS on the table because it is too heavy, or you are too tired to read it. Or cannot take content because it has been taken down in the meantime.
Idea: a time machine in the game gives possibility to go in different versions of the TOS, different views on the same paragraph.
Sidenotes: documentary GET LAMP, early text games gave the feeling of infinite text while having very limited storage.
Proposal: expand the game during a pyweek. http://pyweek.org
Historical Context group
Track main phrases that changes across commits. But what exactly is changing? Can the Markow algorithm help? It could give statistical information on changes.
Interesting to detect high frequency of changes. How to trace the related changes in law? How to analyze what changed? Word frequency?
Proposals:
- Evolution in size of the text of the TOS's
- Evolution of tag clouds over time: the top ten words across commits. Compare tag clouds.