UNIT 1. Introducing DA / Data for DA

1. INTRODUCING DA
1. 1.1. Text and Discourse. What is TL? What is DA?
  1. In everyday popular use, the term "text" is restricted to written language, while "discourse" is restricted to spoken language.
  2. Modern Linguistics: "text" includes every type of utterance; therefore, a text may be a magazine or a tv interview.
  3. Crystal: TL is the formal account of linguistic principles governing the structure of texts.
  4. Beaugrande and Dressler: a text is a communicative event that must satisfy 7 criteria:
    1. 1) Cohesion: relationship between text and syntax; 2) Coherence: related to the meaning of the text, how language is used and the reception influence on the interlocutor; 3) Intentionality: the attitude and purpose of the speaker; 4) Acceptability: the attitude and purpose of the speaker; 5) Informativity: quantity and quality of the information; 6) Situationality: the situation in which the text is produced is significant; 7) Intertextuality: the text is always related to a preceding discourse and texts are always linked and grouped in particular text varieties or genres.
      1. Tischer: the first 2 criteria are text-internal (cohesion and coherence), the rest are text-external. Traditionally, DA pays more attention to the external factors.
        Other authors: text is everything that is meaningful in a particular situation, so texts are viewed as a cognitive process in which context plays a subordinate role. Then, text-internal elements constitute the text and text-external ones are the context.
        Schiffrin: text and context constitute 2 kinds of information that contribute to the communicative content of an utterance.
        Text is the linguistic content (the stable semantic meaning of words, expressions, etc.)
        Context is the world filled with people producing utterances. DA involves the study of both text and context.
  5. Van Dijk and Johnstone: DA is essentially multidisciplinary and crosses many other domains like Poetics, Semiotics, Psychology, Sociology, Anthropology, History, Communication research, Political science, Literary criticism, etc.
  6. DA is the study of language in use, and it studies TEXT and CONTEXT.
2. 1.2. Origins and brief history of TL and DA
  1. A good linguistic description should go beyond the sentence.
  2. In the 20th century, the following disciplines emerged withing the field of Linguistics: Functionalism, Cognitive Linguistics, Sociolinguistics, Pragmatics, Text Linguistics and Discourse Analysis. They are all interrelated.
  3. Important notions: Macrostructure, Cohesion, Strategic understanding (what language really does), Socio-cultural knowledge, Mental models.
3. 1.3. Approaches to the phenomenon of discourse
  1. 3 main categories to define it:
    1. 1) Anything beyond the sentence; 2) Language use; 3) A broad range of socia practice which includes non-linguistic and non-specific instances of language.
  2. Leech and Schiffrin: 2 main approaches, the formal and functional approach
    1. Formal approach: Discourse is language beyond the sentence. Harris was the first to use the term discourse and he was a formalist.
    2. Functional approach: Discourse is language in use.
      1. The purpose and functions of language are important (language and society are parts of each other).
      2. It includes all uses of language, focusing on how language is used to achieve communicative goals.
      3. Discourse is NOT one more level in a hierarchy, but an all-embracing concept which includes: propositional content and social, cultural and contextual contexts.
    3. Schiffrin, on the contrary, proposes too a balanced approach where formal and functional paradigms are integrated: in his view, utterances are units of linguistic production which are inherently contextualized.
  3. Discourse is multimodal because it uses more than one semiotic system.
  4. Wetherell presents 4 approaches to DA:
    1. 1) Language as a system, therefore researches have to find patterns.
    2. 2) Language as a process, not a product. Based on the activity of language use.
    3. 3) The search for language patterns asociated to a given topic or activity.
    4. 4) The search for patterns within broader contexts, such as society or culture.
    5. Common to all: they do not focus on language as abstract product. They are interested in what happens when people use language and how they do things with language (express feeling, extertain others, etc.) That is why it is called DA rather than language analysis.
4. 1.4. What do discourse analysts do?
  1. Discourse analysts investigate the use of language in context; they are interested in what speakers or writers do with it. It has a social dimension.
    1. 1) DA focus on the analytical process in an explicit way. This analysis may be realized by dividing long stretches of discourse into parts of different sort, depending on the research question.
    2. 2) DA provides a great deal of information regarding how hearers or readers interpret what they hear or read. For this, they need to work with texts.
    3. 3) Texts constitute the corpus of the study: transcripts of conversations, written documents, computerized corpus of a language.
    4. 4) Researchers need to acquire some basic knowledge of how to handle the data and to work with corpora.
2. DATA FOR DA
1. 2.1. Data collection
  1. 1) It depends on the research question and type of discourse we want to analyze: written , spoken, its genre...
  2. 2) We have to figure out how to collect the data.
  3. 3) Taylor: the process by which material becomes data is selection. There are different criteria for selecting a sample: fragments of talk, distribution of discourse items, social and cultural information. The goal leads us to different procedures.
2. 2.2. Transcribing the data
  1. 2.2.1. Transcription conventions used by some discourse analysts
    1. 2.2.1.1. Notation used in the London Lund Corpus
      1. The LLC is a computerized corpus of spoken English widely used by linguists and discourse analysts. It consists of 87 texts arranged in text groups.
    2. 2.2.1.2. Other annotations practices
      1. Each researcher chooses his own conventions which depend on the needs and objectives of the analysis.
    3. There are as many ways to transcribe speech as there are researchers, some include more information and some others less. However there is a certain consensus with syntactic categories.
    4. Edwards: 3 main principles should be observed when designing a transcription system:
      1. 1) Categories should be systematically discriminable, exhaustive and systematically contrastive.
      2. 2) Transcripts should be readable.
      3. 3) Mark-up should be systematic and predictable for computational tractability.
  2. Discourse analysts transform the spoken discourse into a document called transcript by means of a process of transcription. That is their data.
    1. To transcribe we use a system of transcription. No neutral transcription exists and it is hard to represent all variables.
      1. Data includes words, aspects of the conversations such as pauses, and sometimes information about the text such as genre, date and place of publications (pronunciation, intonation, sex, age, social class, occupation).
3. 2.3. Ethics of data collection
  1. 1) Researcher has more power than the other participants in the experiement.
  2. 2) Researcher has more information about the experiment than the subjects.
  3. 3) Researcher ought not to abuse their power and has to obtain the consent of the participants.
  4. 4) Researcher has the obligation to protect all participants, not harm them in any way and always observe their legal rights.
4. 2.4. Corpus Linguistics: the use of corpora for DA
  1. 2.4.1. Computer corpora and concordance programs
    1. Concordance programs: turn e-texts into databases that can be searched. Some of them are: Word Cruncher, TACT, SARA, WordSmith Tools
    2. Since the 1980s, large corpora have been compiled. Examples are: The BNC (British National Corpus), The COCA (Corpus of Contemporary American), The ICE (International Corpus of English), The Bank of English, The Shakespeare Online Corpus, The Experimental BNC website, The Davies Corpus.
    3. Reich's classification of corpora (1998) (other variables might be considered depending on the researcher's aims):
      1. a) Medium: Spoken corpora - London Lund Corpus; Written corpora - LOB (Lancaster Oslo/Bergen corpus); Mixed corpora - BNC
      2. b) National varieties: British corpora - LOB; American corpora - Brown corpus, COCA; International corpora - International Corpus of English
      3. c) Historical variation: Diachronic corpora - Helsinki Corpus; Synchronic corpora - LOB, Brown corpus, BNC; Corpora which cover a stage of language history - Shakespeare Corpora, Corpus of OE, Corpus of ME.
      4. d) Geographical/dialectal variation: Corpus of dialect samples - Scots; Mixed corpora - BNC
      5. f) Genre: Literary texts corpora, Technical English corpora, Non-fiction corpora (News texts), Mixed corpora covering all genres.
      6. e) Age: Adult English corpora; Child English corpora - CHILDES
      7. g) Open-endedness: Closed/unalterable corpora - LOB, Brown corpus; Monitor corpora - Bank of England
      8. h) Availability: Commercial corpora, Non-commercial research corpora; Online corpora, corpora on ftp servers, corpora on floppy disks or CD-ROMs
  2. Crystal: a corpus is a collection of linguistic data, either written or transcripted to be used to obtain a hypothesis about a language.
    1. Corpus linguistics has to do with the practice and the principles of using corpora in language study.
  3. Biber: important characteristics of corpus-based analysis are 1) empiricism, 2) utilization of a large collection of natural texts (corpus), 3) use of computer for analysis, and 4) quantitative and qualitative analytical techniques.
  4. The use of corpora: 1) facilitate investigation, 2) allow researchers to analyze patterns of use, 3) allow dealing with large and varied texts bringing more reliability, 4) Computerized corpora permit the storage and analysis of a greater number of natural language texts.

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UNIT 1. Introducing DA / Data for DA

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