1 Reflections on using AI tools to aid in quantitative social science research

Notes on the Future of Quantitative Social Science

Preface
1 Reflections on using AI tools to aid in quantitative social science research
2 Evaluation rules everything around me
3 The Science of Quantitative Social Science in an Age of Artificial Intelligence
4 Teaching data analysis with agentic AI for social science students
5 As AI Lowers the Cost of Research, Adjudication and Attention Will Become the Bottlenecks
6 The Shifting Production Function: AI, Reproducibility, and the Future of Quantitative Social Science
7 All Models Are Wrong, But Some Are Trending
8 Research Transparency and Collaboration in the Era of Generative AI Requires Open, Clean Code
9 Failing Faster, Learning Better: Agentic AI and Empirical Social Science Research
10 A relational approach to agentic AI in social science research: Projects, roles, and tasks
11 Fault Lines: Inequality and the Future of Quantitative Social Science in the Age of Artificial Intelligence
12 My Title
13 My Title
14 AI for Me, Not (Yet) for Thee? Desirable Difficulties and Deliberate Friction with LLMs
15 AI Allows More Diversity in the Forms of Social Science
16 Epistemic Standards and the Next Generation of Scholars
17 With Great Powers: A Practical Guide to Agentic AI for Social Science Research
18 Agentic AI and the Thawing of Frozen Data in Social Science
19 Optimal Editorial Screening Under Cheap Testing
20 Curation and Reproducibility in an Artificial Intelligence World: Challenges and Solutions for Scientific Research
21 On Benchmarks
22 Computational Social Scientists Need to Care About the Competitiveness of the AI Market
References

Table of contents

1.1 Introduction
1.2 Another section

1 Reflections on using AI tools to aid in quantitative social science research

Monica Alexander, University of Toronto

Abstract: TBD

AI usage statement: TBD

1.1 Introduction

TBD

1.2 Another section

2 Evaluation rules everything around me