Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine
- DOI
- 10.5771/2192-4007-2024-1-72
- Published
- 2024
- Container
- Studies in Communication and Media
- Publisher
- Nomos Verlag
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.5771/2192-4007-2024-1-72,
title = {Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine},
author = {Jonas Rieger and Kostiantyn Yanchenko and Mattes Ruckdeschel and Gerret von Nordheim and Katharina Kleinen-von Königslöw and Gregor Wiedemann},
year = {2024},
journal = {Studies in Communication and Media},
doi = {10.5771/2192-4007-2024-1-72},
url = {https://doi.org/10.5771/2192-4007-2024-1-72}
}RIS
TY - JOUR TI - Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine AU - Jonas Rieger AU - Kostiantyn Yanchenko AU - Mattes Ruckdeschel AU - Gerret von Nordheim AU - Katharina Kleinen-von Königslöw AU - Gregor Wiedemann PY - 2024 JO - Studies in Communication and Media DO - 10.5771/2192-4007-2024-1-72 UR - https://doi.org/10.5771/2192-4007-2024-1-72 ER -
APA
Rieger, J., Yanchenko, K., Ruckdeschel, M., Nordheim, G. V., Königslöw, K. K., & Wiedemann, G. (2024). Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine. Studies in Communication and Media. https://doi.org/10.5771/2192-4007-2024-1-72
Source records
- crossref · retrieved 2026-09-26T01:28:41.561Z