Literate execution treats documentation and visualizations as dynamic, computable parts of program execution via provenance tracking, inverting traditional literate programming to make programs more explorable.
Increasing the Transparency of Research Papers with Explorable Multiverse Analyses , booktitle =
5 Pith papers cite this work, alongside 143 external citations. Polarity classification is still indexing.
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Proposes mCCDF plots to visualize ordinal regression results and communicate key takeaways from analyses of ordinal data like Likert scales.
Multiverse analysis of three published CSS studies reveals substantial variation in findings across methodological decision combinations and identifies cases of computational failure not reported in originals.
Introduces PAU as a governance architecture for municipal AI in public spaces, informed by case studies on subgroup-aware scaling (R2=0.89) and pluralistic preference data that treats neutrality as indeterminacy.
citing papers explorer
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Literate Execution
Literate execution treats documentation and visualizations as dynamic, computable parts of program execution via provenance tracking, inverting traditional literate programming to make programs more explorable.
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Adapting CCDF Plots for Visualizing Ordinal Regression Results
Proposes mCCDF plots to visualize ordinal regression results and communicate key takeaways from analyses of ordinal data like Likert scales.
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Making Uncertainty Visible: Multiverse Analysis for Robust Computational Social Science
Multiverse analysis of three published CSS studies reveals substantial variation in findings across methodological decision combinations and identifies cases of computational failure not reported in originals.
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Pluralistic-Alignment Urbanism: Operationalizing a Right to AI for Inclusive Public Space
Introduces PAU as a governance architecture for municipal AI in public spaces, informed by case studies on subgroup-aware scaling (R2=0.89) and pluralistic preference data that treats neutrality as indeterminacy.
- Should We Dangle a Carrot? The Effect of Performance-based Incentives in Visualization Experiments