Making Slides

I was asked to give a short talk in my Departmental Proseminar yesterday on the topic of giving presentations, and specifically about making slides effective. There is more than one way to give a good talk, and there is more than one way to make “good slides” or—better—make good use of slides and other material you might want to show people. So the things I’ll talk about and especially the specific techniques I’ll discuss are selected from many good ways to present yourself and your work. Presenting is a two-sided process, where you should be comfortable with what you are saying (and how you’re saying it), on one side, and where your audience should be able to follow along and understand your message, on the other.

How to Run Surveys: A Guide to Creating Your Own Identifying Variation and Revealing the Invisible

Surveys are an essential approach for eliciting otherwise invisible factors such as perceptions, knowledge and beliefs, attitudes, and reasoning. These factors are critical determinants of social, economic, and political outcomes. Surveys are not merely a research tool. They are also not only a way of collecting data. Instead, they involve creating the process that will generate the data. This allows the researcher to create their own identifying and controlled variation. Thanks to the rise of mobile technologies and platforms, surveys offer valuable opportunities either to study broadly representative samples or to focus on specific groups. This article offers guidance on the complete survey process, from the design of the questions and experiments to the recruitment of respondents and the collection of data to the analysis of survey responses. It covers issues related to the sampling process, selection and attrition, attention and carelessness, survey question design and measurement, response biases, and survey experiments.

How Heavily Are PhD Students Offloading Their Scientific Writing to AI? Evidence from US STEM PhD Dissertations

Large language models are diffusing rapidly into science, raising concerns that delegating research writing to AI may undermine the development of expertise that scientific training is supposed to build. We measure AI-generated writing in the dissertations of a near-census of US STEM PhD graduates from 2019 to 2026 and link graduates to their subsequent employment. AI writing is absent before 2023, present in 29% of dissertations filed in 2026, and rapidly growing. Use is more common among students from non-English-speaking countries and at lower-ranked programs. Among graduates in the same program and cohort, those whose dissertations contain AI writing are less likely to enter academic research careers and more likely to enter non-tenure-track roles or industry, with this pattern strongest among US-origin students. At the same time as AI may increase research productivity, it thus appears to also be displacing a potentially important portion of the work through which scientific expertise is developed.

Where do PhD economists land?

Placement records of 14,000+ graduates from 80 economics departments, 1990–2026. Search by name, department, field and employer.

An LLM Workflow That Reproduces, Improves, and Extends Published Economics Research

We introduce an open-source workflow that enables an LLM to reproduce, improve, and extend an economics article using the article’s published replication package. First, the workflow attempts to reproduce the original calculations, checks for discrepancies with published findings, and performs automated sensitivity analysis. Across 4,452 published replication packages for five economics journals, the workflow flags discrepancies in 3,460 articles or their appendices. Second, the workflow improves the original calculations by using a different implementation or algorithm. In 496 articles, the workflow is able to reduce a calculation’s computation time, at similar or greater accuracy, by more than a factor of 10. Third, the workflow extends the original analysis. In 923 articles, the workflow develops an extension that does not appear in the original article and that is aligned with the original article’s goals and assumptions.

Kevin A. Bryan - Eight Rules for Teaching in AI World

Eight rules for how university teaching should change because of AI.

Report

MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training

The Utility of AI Tools in Auditing Adherence to Pre-Analysis Plans

Pre-analysis plans (PAPs) can improve research reproducibility by reducing researchers’ degrees of freedom, but their value depends on adherence. We argue that large language models (LLMs) can provide an efficient, scalable, and systematic way for authors and reviewers to assess adherence to PAPs. In an application to our own research, an LLM systematically identifies precommitted design choices, evaluates deviations, and diagnoses gaps in pre-specification, substantially reducing the human labor required for these tasks. However, variability in audit output across LLMs underscores the continued importance of human judgment. We discuss implications for best practices in AI-assisted PAP auditing.

Declaration — Math and AI

Read the declaration and add your name.

A protocol for attributable modular research contribution - Matsuthoughts

I wrote this essay in response to Seemay Chou's call for essays on systemic bottlenecks to science. Structural change in scholarly communication practices i…

paulgp/margin

Source-first, comment-only writing review for Markdown, Typst, and LaTeX

modular research preprint server demo with meaningful social context

a concept preprint server demonstrating what kinds of features would be possible in the future with more modular research publishing and an open social protocol to layer in the public discourse in meaningful ways.

AI agents are checking the scientific literature — and spotting decades-old errors

The technology is proving adept at finding faults in decades-old papers and reference databases.

How and When to Involve Crowds in Scientific Research - The Book

This book explores how millions of people can significantly contribute to scientific research. Discover main elements, authors, and who this book is for and why.

The Young Billionaire Behind the War on Bad Science

Meet the former Enron trader and hedge fund founder who's on a quest to expose bad science

science funding and the cherry picking problem

Science publishing–like all of science–requires money. There’s no getting around it, even with the open science movement. What appears free to many authors and readers still requires money and infr…

Agency in the Age of AI

A new special issue and the Robot Boogie