Can We Detect the Effects of Racial Violence on Patenting? Reanalyzing an Article by Lisa Cook · Econ Journal Watch : Reanalysis, replication, conflict, innovation

In 2014, Lisa Cook published an article in Journal of Economic Growth titled, “Violence and Economic Activity: Evidence from African American Pa…

datasetpapers — a public research experiment

An experimental approach to versioned, forkable, machine-readable analyses. A prototype, not a product or service.

datasetpapers — a public research experiment

An experimental approach to versioned, forkable, machine-readable analyses. A prototype, not a product or service.

Stop trusting the extinction math

The AI industry's favourite 'statistic' isn't backed by data. And it distracts from what's really going on.

The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data

Causal inference is a core task of science. However, authors and editors often refrain from explicitly acknowledging the causal goal of research projects; they refer to causal effect estimates as associational estimates. This commentary argues that using the term “causal” is necessary to improve the quality of observational research. Specifically, being explicit about the causal objective of a study reduces ambiguity in the scientific question, errors in the data analysis, and excesses in the interpretation of the results.

The natural selection of bad science

Abstract. Poor research design and data analysis encourage false-positive findings. Such poor methods persist despite perennial calls for improvement, sugg

Konrad Hinsen's blog

Thirty years after my first contact with computational (ir)reproducibility, I am happy to note that many things have improved. Reproducibility, computational and otherwise, is increasingly recognized as an important aspect of scientific quality control, and mostly considered worth striving for. However, I also note that more and more people, including reproducibility activists, have lost contact with the day-to-day reality in which reproducibility matters. Reproducibility is becoming an item on a checklist, and its precise incarnation the subject of political bickering aimed at making it easy to check off that item. So let's take a look at why computational reproducibility matters for researchers.

Apple's Tim Cook makes blistering attack on the 'data industrial complex' | TechCrunch

Apple's CEO Tim Cook has joined the chorus of voices warning that data itself is being weaponized against people and societies -- arguing that the trade

Andreas De Block on Twitter / X

It took Nature three years to publish this rebuttal, which clearly demonstrates the fundamental flaws in the original paper. In the meantime, the paper's conclusions influenced scientific debate and policy decisions. Such delays in correcting flawed research do real damage. https://t.co/QI3Ei3PE70— Andreas De Block (@DeblockBlock) August 13, 2026

America's Digital Demand Threatens Black Communities with More Pollution

The collective costs of artificial intelligence and data centers are disproportionately harming Black households.

Guest Post — When Errors Become Consensus: Science’s Self-Correction Can No Longer Keep Up - The Scholarly Kitchen

From 19th-century beef tea to paper mills and generative AI, the challenge is no longer detection alone; it is preventing bad claims from becoming embedded knowledge.

How the LAPD and Palantir Use Data to Justify Racist Policing

In a new book, a sociologist who spent months embedded with the LAPD details how data-driven policing techwashes bias.

Is the scientific paper still a fraud?

How we write scientific papers does not reflect how we do science. Their formal structure infers a pre-ordained linear process rather than reflecting the messy creativity of research. This matters in the AI age because it masks the human in the process.

Machine Bias — ProPublica

There’s software used across the country to predict future criminals. And it’s biased against blacks.