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Evaluation rules everything around me
Notes on the Future of Quantitative Social Science
Preface
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Evaluation rules everything around me
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AI All The Way Down: Infinite Regress and the Future of Quantitative Social Science
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Teaching data analysis with agentic AI for social science students
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As AI Lowers the Cost of Research, Adjudication and Attention Will Become the Bottlenecks
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The Shifting Production Function: AI, Reproducibility, and the Future of Quantitative Social Science
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All Models Are Wrong, But Some Are Trending
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Research Transparency and Collaboration in the Era of Generative AI Requires Open,
Clean
Code
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Failing Faster, Learning Better: Agentic AI and Empirical Social Science Research
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A relational approach to agentic AI in social science research: Projects, roles, and tasks
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Fault Lines: Inequality and the Future of Quantitative Social Science in the Age of Artificial Intelligence
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My Title
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AI for Me, Not (Yet) for Thee? Desirable Difficulties and Deliberate Friction with LLMs
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Artificial Intelligence and the Measurement Imperative
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AI Allows More Diversity in the Forms of Social Science
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Epistemic Standards and the Next Generation of Scholars
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Quantifying Culture from Embeddings to Generation
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With Great Powers: A Practical Guide to Agentic AI for Social Science Research
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The Human Element in Social Science Research
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Agentic AI and the Thawing of Frozen Data in Social Science
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Optimal Editorial Screening Under Cheap Testing
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Intelligent Machines Are Social Actors
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Curation and Reproducibility in an Artificial Intelligence World: Challenges and Solutions for Scientific Research
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On Benchmarks
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Computational Social Scientists Need to Care About the Competitiveness of the AI Market
References
Table of contents
1.1
Introduction
1
Evaluation rules everything around me
Rohan Alexander
University of Toronto
rohan.alexander@utoronto.ca
Abstract
AI usage statement:
1.1
Introduction
Preface
2
AI All The Way Down: Infinite Regress and the Future of Quantitative Social Science