OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

The political preferences of LLMs
David Rozado
PLoS ONE (2024) Vol. 19, Iss. 7, pp. e0306621-e0306621
Open Access | Times Cited: 10

Showing 10 citing articles:

Assessing political bias and value misalignment in generative artificial intelligence
Fábio Motoki, Valdemar Pinho Neto, Victor Rangel
Journal of Economic Behavior & Organization (2025), pp. 106904-106904
Open Access

Surprising gender biases in GPT
Raluca Alexandra Fulgu, Valerio Capraro
(2024)
Open Access | Times Cited: 2

Digital Dissonance: Large Language Models' Unbalanced Political Narrative
Fábio Motoki, Valdemar Pinho Neto, Victor Rangel
SSRN Electronic Journal (2024)
Closed Access | Times Cited: 1

Emerging AI-Individualism: How Young People Integrate Social AI into Their Lives
Petter Bae Brandtzæg, Marita Skjuve, Asbjørn Følstad
SSRN Electronic Journal (2024)
Closed Access | Times Cited: 1

Surprising gender biases in GPT
Raluca Alexandra Fulgu, Valerio Capraro
Computers in Human Behavior Reports (2024), pp. 100533-100533
Open Access | Times Cited: 1

Understanding model power in social AI
Petter Bae Brandtzæg, Marita Skjuve, Asbjørn Følstad
AI & Society (2024)
Open Access

How Do People React to Political Bias in Generative Artificial Intelligence (AI)?
Uwe Messer
Computers in Human Behavior Artificial Humans (2024), pp. 100108-100108
Open Access

AI as a Research Proxy: Navigating the New Frontier of Social Science Inquiry through Language Models
Antonina Rafikova, А. Н. Воронин
Research Square (Research Square) (2024)
Open Access

Examining the Feasibility of Large Language Models as Survey Respondents
Ayato Kitadai, Kazuhito Ogawa, Nariaki Nishino
2021 IEEE International Conference on Big Data (Big Data) (2024), pp. 3858-3864
Closed Access

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