Matches in SemOpenAlex for { <https://semopenalex.org/work/W2805117413> ?p ?o ?g. }
- W2805117413 endingPage "237" @default.
- W2805117413 startingPage "202" @default.
- W2805117413 abstract "Advances in computer science and computational linguistics have yielded new, and faster, computational approaches to structuring and analyzing textual data. These approaches perform well on tasks like information extraction, but their ability to identify complex, socially constructed, and unsettled theoretical concepts—a central goal of sociological content analysis—has not been tested. To fill this gap, we compare the results produced by three common computer-assisted approaches—dictionary, supervised machine learning (SML), and unsupervised machine learning—to those produced through a rigorous hand-coding analysis of inequality in the news ( N = 1,253 articles). Although we find that SML methods perform best in replicating hand-coded results, we document and clarify the strengths and weaknesses of each approach, including how they can complement one another. We argue that content analysts in the social sciences would do well to keep all these approaches in their toolkit, deploying them purposefully according to the task at hand." @default.
- W2805117413 created "2018-06-13" @default.
- W2805117413 creator A5001694837 @default.
- W2805117413 creator A5011215003 @default.
- W2805117413 creator A5022054335 @default.
- W2805117413 creator A5045942150 @default.
- W2805117413 date "2018-05-27" @default.
- W2805117413 modified "2023-10-01" @default.
- W2805117413 title "The Future of Coding: A Comparison of Hand-Coding and Three Types of Computer-Assisted Text Analysis Methods" @default.
- W2805117413 cites W1493526108 @default.
- W2805117413 cites W1607896850 @default.
- W2805117413 cites W1972375517 @default.
- W2805117413 cites W1974958887 @default.
- W2805117413 cites W1984180862 @default.
- W2805117413 cites W1987971958 @default.
- W2805117413 cites W1997131858 @default.
- W2805117413 cites W2011430131 @default.
- W2805117413 cites W2023614200 @default.
- W2805117413 cites W2028367459 @default.
- W2805117413 cites W2038156033 @default.
- W2805117413 cites W2039941235 @default.
- W2805117413 cites W2051365082 @default.
- W2805117413 cites W2062067126 @default.
- W2805117413 cites W2095655043 @default.
- W2805117413 cites W2097353125 @default.
- W2805117413 cites W2108870400 @default.
- W2805117413 cites W2117667023 @default.
- W2805117413 cites W2119009838 @default.
- W2805117413 cites W2123442489 @default.
- W2805117413 cites W2129018774 @default.
- W2805117413 cites W2140910804 @default.
- W2805117413 cites W2150593711 @default.
- W2805117413 cites W2153383412 @default.
- W2805117413 cites W2154571511 @default.
- W2805117413 cites W2171060319 @default.
- W2805117413 cites W2174706414 @default.
- W2805117413 cites W2182643880 @default.
- W2805117413 cites W2210483295 @default.
- W2805117413 cites W2221871178 @default.
- W2805117413 cites W2251628179 @default.
- W2805117413 cites W2278755290 @default.
- W2805117413 cites W2295260089 @default.
- W2805117413 cites W2320411773 @default.
- W2805117413 cites W2321814056 @default.
- W2805117413 cites W2331249327 @default.
- W2805117413 cites W2341078838 @default.
- W2805117413 cites W2477205605 @default.
- W2805117413 cites W2963459858 @default.
- W2805117413 cites W3124321814 @default.
- W2805117413 cites W3125952890 @default.
- W2805117413 cites W4214517112 @default.
- W2805117413 cites W4233575887 @default.
- W2805117413 cites W4233961384 @default.
- W2805117413 cites W4255732287 @default.
- W2805117413 doi "https://doi.org/10.1177/0049124118769114" @default.
- W2805117413 hasPublicationYear "2018" @default.
- W2805117413 type Work @default.
- W2805117413 sameAs 2805117413 @default.
- W2805117413 citedByCount "62" @default.
- W2805117413 countsByYear W28051174132018 @default.
- W2805117413 countsByYear W28051174132019 @default.
- W2805117413 countsByYear W28051174132020 @default.
- W2805117413 countsByYear W28051174132021 @default.
- W2805117413 countsByYear W28051174132022 @default.
- W2805117413 countsByYear W28051174132023 @default.
- W2805117413 crossrefType "journal-article" @default.
- W2805117413 hasAuthorship W2805117413A5001694837 @default.
- W2805117413 hasAuthorship W2805117413A5011215003 @default.
- W2805117413 hasAuthorship W2805117413A5022054335 @default.
- W2805117413 hasAuthorship W2805117413A5045942150 @default.
- W2805117413 hasConcept C10138342 @default.
- W2805117413 hasConcept C104317684 @default.
- W2805117413 hasConcept C112313634 @default.
- W2805117413 hasConcept C119857082 @default.
- W2805117413 hasConcept C127716648 @default.
- W2805117413 hasConcept C144024400 @default.
- W2805117413 hasConcept C154945302 @default.
- W2805117413 hasConcept C15744967 @default.
- W2805117413 hasConcept C162324750 @default.
- W2805117413 hasConcept C162446236 @default.
- W2805117413 hasConcept C179518139 @default.
- W2805117413 hasConcept C185592680 @default.
- W2805117413 hasConcept C188082640 @default.
- W2805117413 hasConcept C204321447 @default.
- W2805117413 hasConcept C23123220 @default.
- W2805117413 hasConcept C2522767166 @default.
- W2805117413 hasConcept C2775945657 @default.
- W2805117413 hasConcept C36289849 @default.
- W2805117413 hasConcept C41008148 @default.
- W2805117413 hasConcept C55493867 @default.
- W2805117413 hasConcept C63882131 @default.
- W2805117413 hasConcept C77805123 @default.
- W2805117413 hasConceptScore W2805117413C10138342 @default.
- W2805117413 hasConceptScore W2805117413C104317684 @default.
- W2805117413 hasConceptScore W2805117413C112313634 @default.
- W2805117413 hasConceptScore W2805117413C119857082 @default.
- W2805117413 hasConceptScore W2805117413C127716648 @default.
- W2805117413 hasConceptScore W2805117413C144024400 @default.