Reconceptualizing the classification of PNAS articles.
Proc Natl Acad Sci U S A
; 107(49): 20899-904, 2010 Dec 07.
Article
en En
| MEDLINE
| ID: mdl-21078953
ABSTRACT
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.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Publicaciones Periódicas como Asunto
/
Publicaciones
Tipo de estudio:
Prognostic_studies
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Año:
2010
Tipo del documento:
Article
País de afiliación:
Estados Unidos