I think you misunderstand my proposal. I am not describing a fraud classifier. I am describing fine-tuning an embedding space based on papers with known fraud.

I think you misunderstand my proposal. I am not describing a fraud classifier. I am describing fine-tuning an embedding space based on papers with known fraud. The content of the fraudelent paper which includes information such as authorship and other citations can be made to exist on an embedding space. Supervised fine-tuning on labels will alter the shape of that embedding space. The resulting embeddings that would come from such a fine-tuned model would generate different clusterings of papers than what you'd get than if you did not have the labeled fraudelent data at all.