@inproceedings{leto-etal-2024-first,
title = "A First Step towards Measuring Interdisciplinary Engagement in Scientific Publications: A Case Study on {NLP} + {CSS} Research",
author = "Leto, Alexandria and
Roy, Shamik and
Hoyle, Alexander and
Acuna, Daniel and
Pacheco, Maria Leonor",
editor = "Card, Dallas and
Field, Anjalie and
Hovy, Dirk and
Keith, Katherine",
booktitle = "Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.nlpcss-1.11",
doi = "10.18653/v1/2024.nlpcss-1.11",
pages = "144--158",
abstract = "With the rise in the prevalence of cross-disciplinary research, there is a need to develop methods to characterize its practices. Current computational methods to evaluate interdisciplinary engagement{---}such as affiliation diversity, keywords, and citation patterns{---}are insufficient to model the degree of engagement between disciplines, as well as the way in which the complementary expertise of co-authors is harnessed. In this paper, we propose an automated framework to address some of these issues on a large scale. Our framework tracks interdisciplinary citations in scientific articles and models: 1) the section and position in which they appear, and 2) the argumentative role that they play in the writing. To showcase our framework, we perform a preliminary analysis of interdisciplinary engagement in published work at the intersection of natural language processing and computational social science in the last decade.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="leto-etal-2024-first">
<titleInfo>
<title>A First Step towards Measuring Interdisciplinary Engagement in Scientific Publications: A Case Study on NLP + CSS Research</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alexandria</namePart>
<namePart type="family">Leto</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shamik</namePart>
<namePart type="family">Roy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexander</namePart>
<namePart type="family">Hoyle</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="family">Acuna</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="given">Leonor</namePart>
<namePart type="family">Pacheco</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Dallas</namePart>
<namePart type="family">Card</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anjalie</namePart>
<namePart type="family">Field</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dirk</namePart>
<namePart type="family">Hovy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Katherine</namePart>
<namePart type="family">Keith</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Mexico City, Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>With the rise in the prevalence of cross-disciplinary research, there is a need to develop methods to characterize its practices. Current computational methods to evaluate interdisciplinary engagement—such as affiliation diversity, keywords, and citation patterns—are insufficient to model the degree of engagement between disciplines, as well as the way in which the complementary expertise of co-authors is harnessed. In this paper, we propose an automated framework to address some of these issues on a large scale. Our framework tracks interdisciplinary citations in scientific articles and models: 1) the section and position in which they appear, and 2) the argumentative role that they play in the writing. To showcase our framework, we perform a preliminary analysis of interdisciplinary engagement in published work at the intersection of natural language processing and computational social science in the last decade.</abstract>
<identifier type="citekey">leto-etal-2024-first</identifier>
<identifier type="doi">10.18653/v1/2024.nlpcss-1.11</identifier>
<location>
<url>https://aclanthology.org/2024.nlpcss-1.11</url>
</location>
<part>
<date>2024-06</date>
<extent unit="page">
<start>144</start>
<end>158</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T A First Step towards Measuring Interdisciplinary Engagement in Scientific Publications: A Case Study on NLP + CSS Research
%A Leto, Alexandria
%A Roy, Shamik
%A Hoyle, Alexander
%A Acuna, Daniel
%A Pacheco, Maria Leonor
%Y Card, Dallas
%Y Field, Anjalie
%Y Hovy, Dirk
%Y Keith, Katherine
%S Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F leto-etal-2024-first
%X With the rise in the prevalence of cross-disciplinary research, there is a need to develop methods to characterize its practices. Current computational methods to evaluate interdisciplinary engagement—such as affiliation diversity, keywords, and citation patterns—are insufficient to model the degree of engagement between disciplines, as well as the way in which the complementary expertise of co-authors is harnessed. In this paper, we propose an automated framework to address some of these issues on a large scale. Our framework tracks interdisciplinary citations in scientific articles and models: 1) the section and position in which they appear, and 2) the argumentative role that they play in the writing. To showcase our framework, we perform a preliminary analysis of interdisciplinary engagement in published work at the intersection of natural language processing and computational social science in the last decade.
%R 10.18653/v1/2024.nlpcss-1.11
%U https://aclanthology.org/2024.nlpcss-1.11
%U https://doi.org/10.18653/v1/2024.nlpcss-1.11
%P 144-158
Markdown (Informal)
[A First Step towards Measuring Interdisciplinary Engagement in Scientific Publications: A Case Study on NLP + CSS Research](https://aclanthology.org/2024.nlpcss-1.11) (Leto et al., NLP+CSS-WS 2024)
ACL