The use of ChatGPT for identifying disruptive papers in science
A first exploration
Bibliographic Data
| ID | 21443085 |
|---|---|
| Authors | Lutz Bornmann (0000-0003-0810-7091, Max Planck Society, corresponding author), Lingfei Wu (0000-0002-0102-3613, University of Pittsburgh), Christoph Ettl |
| Year | 2024 |
| Volume | 129 |
| Issue | 11 |
| Pages | 7161-7165 |
| Publication date | 2024-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Scientometrics (JOURNAL) |
| Journal identifiers | ISSN: 0138-9130 • E-ISSN: 1588-2861 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11192-024-05176-z |
| OpenAlex | W4403377182 |
| Language | EN |
| Citations received | 6 |
| References cited | 15 |
ChatGPT has arrived in quantitative research evaluation. With the exploration in this Letter to the Editor, we would like to widen the spectrum of the possible use of ChatGPT in bibliometrics by applying it to identify disruptive papers. The identification of disruptive papers using publication and citation counts has become a popular topic in scientometrics. The disadvantage of the quantitative approach is its complexity in the computation. The use of ChatGPT might be an easy to use alternative
Data science · Sociology · Artificial Intelligence in Healthcare and Education · Computer Science · Explainable Artificial Intelligence (XAI · Misinformation and Its Impacts
Opinion paper
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Utilizing large model for content-based identification of disruptive and developmental research
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Large teams develop and small teams disrupt science and technology
The use of ChatGPT to find similar institutions for institutional benchmarking
What do we know about the disruption index in scientometrics? An overview of the literature
Can ChatGPT be used to predict citation counts, readership, and social media interaction? An exploration among 2222 scientific abstracts
| Citations per year | 6 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |