Skip to content

Update references and DOI to published article #24

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
May 19, 2025
Merged

Update references and DOI to published article #24

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -143,4 +143,4 @@ The repository currently implements the methods developed in the following paper

[Prediction-Powered Bootstrap](https://arxiv.org/abs/2405.18379)

[The Mixed Subjects Design: Treating Large Language Models as (Potentially) Informative Observations](https://osf.io/preprints/socarxiv/j3bnt)
[The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations](https://doi.org/10.1177/00491241251326865)
2 changes: 1 addition & 1 deletion examples/power_analysis.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@
"References \n",
"* [1] Angelopoulos, A. N., Bates, S., Fannjiang, C., Jordan, M. I., & Zrnic, T. (2023). Prediction-powered inference. Science, 382(6671), 669-674. https://www.science.org/doi/full/10.1126/science.adi6000 \n",
"* [2] Angelopoulos, A. N., Duchi, J. C., & Zrnic, T. (2023). PPI++: Efficient prediction-powered inference. https://arxiv.org/abs/2311.01453\n",
"* [3] Broska, D., Howes, M., & van Loon, A. (2024). The Mixed Subjects Design: Treating Large Language Models as (Potentially) Informative Observations. https://doi.org/10.31235/osf.io/j3bnt\n",
"* [3] Broska, D., Howes, M., & van Loon, A. (2025). The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations. Sociological Methods & Research. https://doi.org/10.1177/00491241251326865\n",
"\n"
]
},
Expand Down
Loading