The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations

This repository provides the replication code and data for the journal article titled "The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations." Our two statistical contributions-the PPI correlation and the PPI power analysis-are implemented in the ppi_py Python library: https://github.com/aangelopoulos/ppi_py/ Please refer to GitHub if you want to clone our repository: https://github.com/davidbroska/MixedSubjects

Publication Details

Journal
OSF Preprints (OSF Preprints)
Published
2025-01-01
DOI
https://doi.org/10.17605/osf.io/c2rjp
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
0.00
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article

The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations

OSF Preprints (OSF Preprints)
Computational and Text Analysis Methods
article

The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations

article en

Abstract

This repository provides the replication code and data for the journal article titled "The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations." Our two statistical contributions-the PPI correlation and the PPI power analysis-are implemented in the ppi_py Python library: https://github.com/aangelopoulos/ppi_py/ Please refer to GitHub if you want to clone our repository: https://github.com/davidbroska/MixedSubjects

OSF Preprints (OSF Preprints)
Quality Education
Openalex Percentile: Top 42%
Computational and Text Analysis Methods
DOIOpen Access PDFOpenAlex
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The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations · OSF Preprints (OSF Preprints) (2025) | TGRS Research Map | TGRS