2
Evaluation rules everything around me
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
2.1
Introduction
2.2
Another section
2
Evaluation rules everything around me
Rohan Alexander,
University of Toronto
Abstract:
TBD
AI usage statement:
TBD
2.1
Introduction
TBD
2.2
Another section
TBD
1
Reflections on using AI tools to aid in quantitative social science research
3
The
Science
of Quantitative Social Science in an Age of Artificial Intelligence