Scaling Synthesis
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Joel Chan
Last updated March 18, 2023
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Atlas.TI
Breaking ideas down into component parts facilitates creative reinterpretation
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Compression facilitates synthesis
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Context is necessary for knowledge reuse
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Discourse graphs could significantly accelerate human synthesis work
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Effective individual synthesis systems (seem to mostly) exist (for a select few)
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Effective synthesis is necessary for innovation and scientific progress
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End user programming enables people to bulk process notes
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Hypertext enables communication with high information density
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People naturally try to enact typed distinctions in their notes
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People process complex information in multiple levels and stages of processing
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Scholarly argumentation operates on atomic statements and concepts as fundamental units
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Synthesis as a process is usefully modeled as a specialized form of sensemaking
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Synthesis is hard to do with people who don’t share context with you
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Synthesis tools need to support incremental formalization
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Search terms as the basis for user-defined subscriptions
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searchable blobs as unnamed groups enable unnamed formalization
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Learning Loop Complex
LiquidText
NVivo
Notional Model of Sensemaking
How do we enable many users with their own idiosyncratic structure to converge in one discourse graph?
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How might we navigate the structure now or later tradeoffs?
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What are the most efficient routes to useful cross-boundary knowledge?
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What is a decentralized discourse graph?
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What is synthesis?
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What is the role of AI in facilitating a decentralized discourse graph?
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What user behaviors are scholars doing already that specify structure for synthesis?
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Accelerating Scientific Discovery by Lowering Barriers to User-Generated Synthesis of Scientific Literature
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Collaborative information synthesis
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Context Grabbing Assigning Metadata in Large Document Collections
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Information and Context
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Knowledge Synthesis- a conceptual model and practical guide
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The common forms of structure people add to their notes