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Part of
Corpora and Rhetorically Informed Text Analysis: The diverse applications of DocuScope
Edited by David West Brown and Danielle Zawodny Wetzel
[Studies in Corpus Linguistics 109] 2023
► pp. 291–292

Subject index

A

  • Agglomerative Hierarchical Clustering (AHC) 274–75, 278
  • American Geophysical Union (AGU) 214–16, 219, 221, 225, 229
  • American Library Association (ALA) 266–68, 281
  • Association for Writing Across the Curriculum’s (AWAC)120
  • audiences 9, 11, 26, 121, 124–26, 215–18, 220–21, 224, 227–30, 232–33, 252, 254–55, 281
  • automated text analysis246

B

  • British Academic Written English (BAWE) 42–43, 46–47, 58–60, 68, 71, 73, 75

C

  • children’s literature 192, 264–65, 268–69, 273–74, 277, 279
  • close reading 149, 220, 227–28, 241, 245, 271
  • cohesion 156, 199, 204, 206–8
  • computational rhetoric 25, 168
  • Conference on College Composition and Communication (CCCC)119
  • confidence, expressions of 10, 12, 280
  • corpus analysis 28–29, 42, 94, 119–20, 151, 218
  • Corpus of Contemporary American English (COCA) 13–15, 29

D

  • digital humanities 25, 167, 170, 240, 264, 281
  • directed self-placement (DSP) 102, 104, 109, 111
  • disciplines, writing in 25, 44, 46–49, 51, 53–59, 62–64, 66, 70–71, 74–75, 103, 122–23, 214–15, 217
  • discourse communities 31, 33, 36, 122, 164
  • DocuScope
    • categories 17, 79, 84, 104, 106–7, 111, 113, 115, 272, 274, 276–77, 284–85
    • dictionaries 6–8, 10–11, 20, 28, 32, 151–53, 155–62, 168–69, 174, 273–74, 284–285
    • taxonomy 148, 150, 158, 160–62
  • DocuScope Write & Audit 17, 32, 34, 121, 192, 194–95, 198–201, 203–6, 208–11

E

  • early feedback 192–93, 196, 201–4, 207–11

F

  • factor analysis (FA) 16, 46, 167, 170–73, 177, 219, 248–49

G

  • gendered readership 264–65, 272–74, 278–79
  • genre 5–6, 30, 32–33, 36, 44, 79–83, 86–89, 101–3, 111, 121–22, 154, 156, 192–93, 211, 244
  • genre analysis 30, 79, 192
  • grammar 5, 36, 79, 94, 97, 113

I

  • information, given-new 208, 210
  • informational density 109–10, 229
  • interpretability 148, 155, 162, 164, 171, 184

L

  • Language Action Types (LATs) 28, 81, 152, 167–72, 177–78, 183–85, 187
  • large language models (LLMs) 162, 164–65
  • Latent Dirichlet Allocation (LDA) 167–70, 173, 181, 187
  • Linguistic Inquiry and Word Count (LIWC)27

M

  • machine learning 148, 150, 152–53, 174, 223–24
  • Michigan Corpus of Upper-level Student Papers (MICUSP) 42, 44–47, 51, 53, 57–59, 67–68, 70–73, 75–76, 102–7, 109, 111, 113, 192, 196
  • Multi-Dimensional Analysis (MDA) 44, 46, 48

N

  • narrative writing 12, 31, 65, 89, 101–2, 230
  • natural language processing (NLP) 22, 25, 27, 38, 148, 150, 156, 158, 164–65, 173, 176
  • Newsgroups 32, 167, 169, 172–80, 182, 184, 186
  • non-negative matrix factorization (NMF) 167–70, 172–75, 177–78, 181, 183

P

  • pedagogy 5, 31, 34, 36–37, 120, 124, 164
  • plain language summaries (PLS) 36, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232
  • polysemy 10–12, 29
  • postsecondary writing 94–96, 101–3, 105–10, 113–15
  • principal component analysis (PCA) 160, 168, 170–73, 177, 187, 265, 274–75
  • proposal writing 102–3, 125, 193–204, 206–8, 211, 244, 253, 259
  • public policy research 32, 150

Q

  • qualitative analysis 120, 125, 152, 156

R

  • RAND-Lex 104, 151–52, 154–55, 158
  • reader experience 6, 30, 120, 192
  • registers 4–5, 37, 120–21, 240, 243–45, 249, 251, 254–55, 257–58
  • rhetorical analysis 17, 33, 42, 81, 178, 216

S

  • science communication214
  • secondary writing 95, 102, 108, 111, 113, 115
  • sentiment analysis 29–30, 156, 183
  • social justice 32, 119–24, 126, 128, 130, 135–36, 138, 217
  • stance 32, 38, 50–51, 70, 111, 116–17, 148, 152–55, 158, 160, 162–64
    • categories 152, 161, 164
  • statistics
    • descriptive 83, 131, 145
    • social justice 127, 130, 133–38, 144
  • strategic language 57, 72, 74, 240–63
  • student writing 31, 34, 36, 42, 94–95, 99, 114, 116–17, 120, 194, 196

T

  • taggers 25, 27–28, 44–46, 49, 51–52, 54, 75, 120, 243
    • comparison of 45, 50
    • dictionary-based 44, 265
    • linguistic 28, 37, 46, 52–57, 76
    • rhetorical 264–65
  • technical communication 123, 125–26, 216–17
  • text mining 32, 167, 244, 264
  • topical analysis 16–17, 35, 192–94, 199, 211
  • topic modeling 36, 157, 167–70, 173–74, 282

V

  • variation
    • disciplinary 42–45, 48, 76, 192
    • register 44, 243
    • role-identity244
    • text-type 265, 272
    • within-genre 81–82, 86–87

W

  • Writing in the Disciplines (WID) 31, 42–43, 139