Novelty and Impact of Economics Papers
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912709979668480 |
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| author | Wu, Chaofeng |
| author_facet | Wu, Chaofeng |
| contents | We propose a framework that recasts scientific novelty not as a single attribute of a paper, but as a reflection of its position within the evolving intellectual landscape. We decompose this position into two orthogonal dimensions: \textit{spatial novelty}, which measures a paper's intellectual distinctiveness from its neighbors, and \textit{temporal novelty}, which captures its engagement with a dynamic research frontier. To operationalize these concepts, we leverage Large Language Models to develop semantic isolation metrics that quantify a paper's location relative to the full-text literature. Applying this framework to a large corpus of economics articles, we uncover a fundamental trade-off: these two dimensions predict systematically different outcomes. Temporal novelty primarily predicts citation counts, whereas spatial novelty predicts disruptive impact. This distinction allows us to construct a typology of semantic neighborhoods, identifying four archetypes associated with distinct and predictable impact profiles. Our findings demonstrate that novelty can be understood as a multidimensional construct whose different forms, reflecting a paper's strategic location, have measurable and fundamentally distinct consequences for scientific progress. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Novelty and Impact of Economics Papers Wu, Chaofeng General Economics Economics Computational Engineering, Finance, and Science Computation and Language Digital Libraries We propose a framework that recasts scientific novelty not as a single attribute of a paper, but as a reflection of its position within the evolving intellectual landscape. We decompose this position into two orthogonal dimensions: \textit{spatial novelty}, which measures a paper's intellectual distinctiveness from its neighbors, and \textit{temporal novelty}, which captures its engagement with a dynamic research frontier. To operationalize these concepts, we leverage Large Language Models to develop semantic isolation metrics that quantify a paper's location relative to the full-text literature. Applying this framework to a large corpus of economics articles, we uncover a fundamental trade-off: these two dimensions predict systematically different outcomes. Temporal novelty primarily predicts citation counts, whereas spatial novelty predicts disruptive impact. This distinction allows us to construct a typology of semantic neighborhoods, identifying four archetypes associated with distinct and predictable impact profiles. Our findings demonstrate that novelty can be understood as a multidimensional construct whose different forms, reflecting a paper's strategic location, have measurable and fundamentally distinct consequences for scientific progress. |
| title | Novelty and Impact of Economics Papers |
| topic | General Economics Economics Computational Engineering, Finance, and Science Computation and Language Digital Libraries |
| url | https://arxiv.org/abs/2511.01211 |