RUcore: Rutgers University Community Repository
Metadata and Modeling Outputs for study "The Quiet Transformations of Literary Studies". Data
Descriptive
Genre
(authority = RURes_researchMethodology)
Quantitative research
Genre
(authority = RURes_dataGenre)
Experimental data
Genre
(authority = RURes_dataLifecycle)
Repurposed data
Genre
(authority = RURes_Genre)
Research data
Subject
(authority = RURes_subjectOfStudy)
Topic
Creative works
Name
(type = personal)
NamePart
(type = family)
Goldstone
NamePart
(type = given)
Andrew
NamePart
(type = termsOfAddress)
Dr.
Affiliation
Rutgers University
Role
RoleTerm
(type = text);
(authority = RULIB)
principal investigator
Name
(type = personal)
NamePart
(type = family)
Underwood
NamePart
(type = given)
Ted
NamePart
(type = termsOfAddress)
Dr.
Affiliation
University of Illinois, Champaign-Urbana
Role
RoleTerm
(type = text);
(authority = RULIB)
principal investigator
TypeOfResource
Dataset
TitleInfo
Title
Metadata and Modeling Outputs for study "The Quiet Transformations of Literary Studies". Data
Classification
(authority = RUresearch);
(edition = Data)
Abstract
(type = abstract)
New analytical approaches, like topic modeling, can illuminate subtle transformations, revealing concepts, frequently taken for granted, to be more variable than scholars have assumed. In this study, the corpus that was modeled included 21,367 JSTOR articles and 13,221 distinct author names resulting in the 150-topic model.
The four files supporting this study and available here are: 1) vocab.txt: UTF-8 text, one word per line, giving all 98835 word types included in the model. The list of stop words excluded from this vocabulary is given at https://www.ideals.illinois.edu/handle/2142/45709, 2) id_map.txt: UTF-8 text, one string per line, giving JSTOR ID strings of all 23167 documents included in the model, in the order indexed by the sampling state file, 3) mallet_state.gz (370MB): gzip'd UTF-8 text representing the final sampling state output by MALLET. Each token of the input documents is represented by a single line, with six white-space delimited fields: document index, document label (unused), token index, word type index, word type as a string, topic index. The word type index is zero-based and corresponds to the order in vocab.txt. The document index is zero-based and corresponds to the order in id_map.txt, and 4) metadata.tar.gz (3.9MB): gzip'd tar archive of 8 CSV files containing metadata for the documents modeled. Metadata for documents in the model can be located by matching the "id" column to the IDs given in id_map.txt.
The four files supporting this study and available here are: 1) vocab.txt: UTF-8 text, one word per line, giving all 98835 word types included in the model. The list of stop words excluded from this vocabulary is given at https://www.ideals.illinois.edu/handle/2142/45709, 2) id_map.txt: UTF-8 text, one string per line, giving JSTOR ID strings of all 23167 documents included in the model, in the order indexed by the sampling state file, 3) mallet_state.gz (370MB): gzip'd UTF-8 text representing the final sampling state output by MALLET. Each token of the input documents is represented by a single line, with six white-space delimited fields: document index, document label (unused), token index, word type index, word type as a string, topic index. The word type index is zero-based and corresponds to the order in vocab.txt. The document index is zero-based and corresponds to the order in id_map.txt, and 4) metadata.tar.gz (3.9MB): gzip'd tar archive of 8 CSV files containing metadata for the documents modeled. Metadata for documents in the model can be located by matching the "id" column to the IDs given in id_map.txt.
Extension
DescriptiveEvent
Type
Related publication
Label
Article discusses experimental outcomes. Note appendix on technical details.
DateTime
(point = start);
(encoding = w3cdtf);
(qualifier = exact)
2014
Detail
Goldstone, A., & Underwood, T. "The Quiet Transformations of Literary Studies: What Thirteen
Thousand Scholars Could Tell Us", Rutgers University Community Repository, 2014. DOI:
http://dx.doi.org/doi:10.7282/T3222RZT
Thousand Scholars Could Tell Us", Rutgers University Community Repository, 2014. DOI:
http://dx.doi.org/doi:10.7282/T3222RZT
OriginInfo
DateCreated
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2014
Place
PlaceTerm
(type = code)
TableOfContents
The CVS archive is structured for convenient access by the data-analysis scripts at http://github.com/agoldst/tmhls, with each CSV file stored in its own directory:
elh_ci_all/citations.CSV,
mlr1905-1970/citations.CSV,
mlr1971-2013/citations.CSV,
modphil_all/citations.CSV,
nlh_all/citations.CSV,
pmla_all/citations.CSV,
res1925-1980/citations.CSV,
res1981-2012/citations.CSV.
elh_ci_all/citations.CSV,
mlr1905-1970/citations.CSV,
mlr1971-2013/citations.CSV,
modphil_all/citations.CSV,
nlh_all/citations.CSV,
pmla_all/citations.CSV,
res1925-1980/citations.CSV,
res1981-2012/citations.CSV.
RelatedItem
(type = host)
TitleInfo
Title
Literary Studies Topic Modeling: Metadata and Modeling Outputs
Identifier
(type = local)
rucore00000002286
Location
PhysicalLocation
(authority = marcorg);
(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T35B00V9
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Rights
RightsDeclaration
(AUTHORITY = RU_Research);
(ID = RU_Research001)
Copyright for research resources published in RUcore is retained by the copyright holder. By virtue of its appearance in this open access medium, you are free to use this resource, with proper attribution, in educational and other non-commercial settings. Other uses, such as reproduction or republication, may require the permission of the copyright holder.
Availability
Status
Open
Reason
Released to public domain
RightsEvent
Type
Permission or license
Label
JSTOR Terms and Conditions of Use
DateTime
(encoding = iso8601);
(point = start);
(qualifier = exact)
September 3, 2014
AssociatedEntity
Role
Licensor
Name
JSTOR
AssociatedObject
Type
License
Reference
(type = digital)
Detail
http://www.jstor.org/page/info/about/policies/terms.jsp#TC1
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Technical
RULTechMD
(ID = TECHNICAL1)
ContentModel
Dataset
RULTechMD
(ID = TECHNICAL2)
ContentModel
Dataset
RULTechMD
(ID = TECHNICAL3)
ContentModel
Dataset
RULTechMD
(ID = TECHNICAL4)
ContentModel
Dataset
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Statistical Profile