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Cited by 328

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

The Measurement of Intellectual Influence

Open access
Palacios-Huerta, Ignacio, Volij, Oscar·2004·Econometrica

Abstract

We examine the problem of measuring influence based on the information contained in the data on the communications between scholarly publications, judicial decisions, patents, web pages, and other entities. The measurement of influence is useful to address several empirical questions such as reputation, prestige, aspects of the diffusion of knowledge, the markets for scientists and scientific publications, the dynamics of innovation, ranking algorithms of search engines in the World Wide Web, and others. In this paper we ask why any given methodology is reasonable and informative applying the axiomatic method. We find that a unique ranking method can be characterized by means of five axioms: Anonymity, invariance to citation intensity, weak homogeneity, weak consistency, and invariance to splitting of journals. This method is easily implementable and turns out to be different from those regularly used in social and natural sciences, arts and humanities, and computer science.

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Crossref · 10.1111/j.1468-0262.2004.00519.x

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Metadata

Authors
Palacios-Huerta, I., & Volij, O.
Venue
Econometrica
Published
2004-05
Volume
72
Issue
3
Pages
963-977
ISSN
  • 0012-9682 (ISSN-L)
  • 1468-0262
OpenAlex
W3125814070
Cited by
328
References
1
FWCI
12.63
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: The Measurement of Intellectual Influence. https://app.cochat.ai/papers/10.1111/j.1468-0262.2004.00519.x
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Palacios-Huerta, I., & Volij, O. (2004). The Measurement of Intellectual Influence. Econometrica. https://doi.org/10.1111/j.1468-0262.2004.00519.x

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.