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Reconceptualizing the classification of PNAS articles

PNAS article classification is rooted in long-standing disciplinary divisions that do not necessarily reflect the structure of modern scientific research. We reevaluate that structure using latent pattern models from statistical machine learning, also known as mixed-membership models, that identify... Full description

Journal Title: Proceedings of the National Academy of Sciences of the United States of America 07 December 2010, Vol.107(49), pp.20899-904
Main Author: Airoldi, Edoardo M
Other Authors: Erosheva, Elena A , Fienberg, Stephen E , Joutard, Cyrille , Love, Tanzy , Shringarpure, Suyash
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1091-6490 ; PMID: 21078953 Version:1 ; DOI: 10.1073/pnas.1013452107
Link: http://pubmed.gov/21078953
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recordid: medline21078953
title: Reconceptualizing the classification of PNAS articles
format: Article
creator:
  • Airoldi, Edoardo M
  • Erosheva, Elena A
  • Fienberg, Stephen E
  • Joutard, Cyrille
  • Love, Tanzy
  • Shringarpure, Suyash
subjects:
  • Periodicals As Topic -- Classification
  • Publications -- Classification
ispartof: Proceedings of the National Academy of Sciences of the United States of America, 07 December 2010, Vol.107(49), pp.20899-904
description: PNAS article classification is rooted in long-standing disciplinary divisions that do not necessarily reflect the structure of modern scientific research. We reevaluate that structure using latent pattern models from statistical machine learning, also known as mixed-membership models, that identify semantic structure in co-occurrence of words in the abstracts and references. Our findings suggest that the latent dimensionality of patterns underlying PNAS research articles in the Biological Sciences is only slightly larger than the number of categories currently in use, but it differs substantially in the content of the categories. Further, the number of articles that are listed under multiple categories is only a small fraction of what it should be. These findings together with the sensitivity analyses suggest ways to reconceptualize the organization of papers published in PNAS.
language: eng
source:
identifier: E-ISSN: 1091-6490 ; PMID: 21078953 Version:1 ; DOI: 10.1073/pnas.1013452107
fulltext: fulltext
issn:
  • 10916490
  • 1091-6490
url: Link


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descriptionPNAS article classification is rooted in long-standing disciplinary divisions that do not necessarily reflect the structure of modern scientific research. We reevaluate that structure using latent pattern models from statistical machine learning, also known as mixed-membership models, that identify semantic structure in co-occurrence of words in the abstracts and references. Our findings suggest that the latent dimensionality of patterns underlying PNAS research articles in the Biological Sciences is only slightly larger than the number of categories currently in use, but it differs substantially in the content of the categories. Further, the number of articles that are listed under multiple categories is only a small fraction of what it should be. These findings together with the sensitivity analyses suggest ways to reconceptualize the organization of papers published in PNAS.
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titleReconceptualizing the classification of PNAS articles
descriptionPNAS article classification is rooted in long-standing disciplinary divisions that do not necessarily reflect the structure of modern scientific research. We reevaluate that structure using latent pattern models from statistical machine learning, also known as mixed-membership models, that identify semantic structure in co-occurrence of words in the abstracts and references. Our findings suggest that the latent dimensionality of patterns underlying PNAS research articles in the Biological Sciences is only slightly larger than the number of categories currently in use, but it differs substantially in the content of the categories. Further, the number of articles that are listed under multiple categories is only a small fraction of what it should be. These findings together with the sensitivity analyses suggest ways to reconceptualize the organization of papers published in PNAS.
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abstractPNAS article classification is rooted in long-standing disciplinary divisions that do not necessarily reflect the structure of modern scientific research. We reevaluate that structure using latent pattern models from statistical machine learning, also known as mixed-membership models, that identify semantic structure in co-occurrence of words in the abstracts and references. Our findings suggest that the latent dimensionality of patterns underlying PNAS research articles in the Biological Sciences is only slightly larger than the number of categories currently in use, but it differs substantially in the content of the categories. Further, the number of articles that are listed under multiple categories is only a small fraction of what it should be. These findings together with the sensitivity analyses suggest ways to reconceptualize the organization of papers published in PNAS.
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