Decoding Knowledge Claims: The Evaluation of Scientific Publication Contributions through Semantic Analysis
<|reference_start|>Decoding Knowledge Claims: The Evaluation of Scientific Publication Contributions through Semantic Analysis: The surge in scientific publications challenges the use of publication counts as a measure of scientific progress, requiring alternative metrics that emphasize the quality and novelty of scientific contributions rather than sheer quantity. This paper proposes the use of Relaxed Word Mover's Distance (RWMD), a semantic text similarity measure, to evaluate the novelty of scientific papers. We hypothesize that RWMD can more effectively gauge the growth of scientific knowledge. To test such an assumption, we apply RWMD to evaluate seminal papers, with Hirsch's H-Index paper as a primary case study. We compare RWMD results across three groups: 1) H-Index-related papers, 2) scientometric studies, and 3) unrelated papers, aiming to discern redundant literature and hype from genuine innovations. Findings suggest that emphasizing knowledge claims offers a deeper insight into scientific contributions, marking RWMD as a promising alternative method to traditional citation metrics, thus better tracking significant scientific breakthroughs.<|reference_end|>
@article{d'aniello2024decoding,
    title={Decoding Knowledge Claims: The Evaluation of Scientific Publication
  Contributions through Semantic Analysis},
    author={Luca D'Aniello, Nicolas Robinson-Garcia, Massimo Aria, Corrado
  Cuccurullo},
    journal={arXiv preprint arXiv:2407.18646},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18646},
    primaryClass={cs.DL cs.IR}
}