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A tiny ontology machine, based on Ian Bogost's concepts of "tiny ontologies" and "flat ontology," is a tool used in TUG's six-week Introduction to IA class because it provides a foundational, practical lens for analysis and discovery.
The core utility lies in the practice of ontography, which the class uses as an "inscriptive strategy" to uncover the "repleteness of units and their interobjectivity". In an introductory course, this approach is invaluable as it teaches students to resist the reflex of premature hierarchical categorization.
The machine—which facilitates exercises like Latour's litanies and is referred to in class notes as a "speculative ontography tool"—is useful because it forces a posture of "defamiliarization". This act of "agitation and provocation" helps students to see an environment not by its inherited categories, but by its latent structural logic and "togetherings".
By using this tool to explore relationships among entities that aren't apparent in vivo students learn to surface upstream meaning structures before converging on final taxonomies, making it a critical foundation for all subsequent IA work.
JavaScript Array Format:
const wordPool = [
"word1",
"word2",
"word3"
];
JSON Array Format:
["word1", "word2", "word3"]
Plain Text Format:
word1 word2 word3
Files can have .js, .json, or .txt extensions. The tool will automatically detect and parse the format.
Version 2.0
A tool for exploring ontological phenomena among and within a large corpus of words.
The default dataset was sourced from Project Gutenberg's text of the 1922 edition of James Joyce's Ulysses. You can replace this with your own specialized vocabulary using the Load Data function.
Smart Word Selection: Instead of picking words randomly, the algorithm favors terms with "ontological weight" - specific over generic (lighthouse rather than building), concrete over abstract (hammer rather than tool), and evocative over bland (precipice rather than hill).
Intelligent Sequencing: Rather than simply shuffling words, it arranges them to create maximum surprise and prevent narrative flow. Adjacent words are chosen to be "alien" to each other - different scales, different types of things - creating the jarring, thought-provoking quality that makes litanies philosophically powerful.
Friction-Driven Assembly: The algorithm actively seeks combinations that create intellectual friction and scale-jumping surprises, ensuring your litanies mix quarks with corporations, storms with algorithms, in ways that resist comfortable categorization.
Bogostian Restraint: Following Bogost's principles, adjectives are used sparingly (only 8% of the time) and only when they add genuine ontological depth rather than mere description. The length is optimized to 7-10 items for proper "ontological density."