# Novelty and Impact of Economics Papers

arXiv: https://arxiv.org/abs/2511.01211 · HTML: https://mlsanity.com/paper/2511.01211/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [Using Large Language Models to Automate Category and Trend Analysis of Scientific Articles: An Application in Ophthalmology](https://mlsanity.com/paper/2308.16688/) (2023)
- [WisPerMed at BioLaySumm: Adapting Autoregressive Large Language Models for Lay Summarization of Scientific Articles](https://mlsanity.com/paper/2405.11950/) (2024)
- [CCSBench: Evaluating Compositional Controllability in LLMs for Scientific Document Summarization](https://mlsanity.com/paper/2410.12601/) (2024)

## Abstract (arXiv)

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: textitspatial novelty, which measures a paper's intellectual distinctiveness from its neighbors, and textittemporal 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.