The macroanalysis and selection of a subcorpus for subsequent microanalysis:

CHAPTER 5. THE STUDY

5.2 The macroanalysis and selection of a subcorpus for subsequent microanalysis:

The next step was an analysis of the macrostructure of the presentations, which provided an insight into their topic flow and moves structure (Swales 1990; Carter-Thomas and Jowley- Rolivet 2003). From this analysis, integrated with the information obtained from the interviews with speakers and observation sheets, a series of potentially persuasive points were identified, which I have called rich points. The term rich point was coined by Agar (1996) and it has become a common concept in ethnographic studies. It refers to moments of experience that stand out from the rest because they reveal a cultural difference, to the point that they often imply a breakdown in cross-cultural communication. This makes them worthy of attention as a research focus. However, I am using the term in a different way. My rich points are moments in the presentation which are particularly rich in terms of persuasive efforts from the speakers. Their length varies in each case, since the contours were established according to a sense of completeness: they seem to fulfil a communicative intention or objective (e.g. illustrates a claim, prompt the audience to action, etc.). For example, in the following rich point from a product pitch the speaker is describing his product and listing its

152 unique characteristics (see Appendix 3 PPPA2 for the full transcription of the four modes; the coding system for naming rich points will be explained in the next section). The contours of this rich point have been set according to when this description begins and ends (i.e. without interrupting the list with two items).

Example of Rich Point contours:

Uxpro is an analytic service that integrates in your app and helps to understand and measure your users’ experience and how your users feel about that app. It includes two important concepts. First behaviour and satisfaction tracking in real time. What your users are doing, whether they are failing, whether they are succeeding, how long they need to do things, how they feel about that app. And secondly, really important thing, because the combination of these two things is the real magic. It’s, it’s collection of users’ sentiment in a very pragmatic way with feedback and microsurvey. Letting your users tell you how they feel about how they are doing with your app.

It must be stressed that the rich points were not selected because they illustrated a particular use of words or a specific paralinguistic or kinesic feature, but exclusively on the grounds of strong persuasive effort. In the previous example, the rich point was not chosen because it featured an example of a list or intense language like “magic” (this was noted after closer analysis), but because it was considered persuasive.

The identification, delimitation and selection of rich points was what Goldman et al. (2007) would call an inductive approach to video data:

Inductive approaches are considered more apt for sampling when working with 'raw’ video data sets that have been collected with broad questions in mind but without a strong orienting theory. The process is usually to view all of the video data repeatedly and in increasing depth where the research team agree on major events, themes and identify key moments of importance and describe the structure of the event. (Jewitt 2012: 19)

The process was supported by three sources. The first one was the information gathered from the observation sheets and the first interviews with speakers (e.g. their explanations of what was their main goal with their presentations). For example in one of the research dissemination talks (see Appendix 3 DTLA1), the rich points identified correspond to moments in which the speaker tries to convince the audience that a better world is possible with a change of attitude. During the interview, the speaker explained that she had been inspired by a TED talk about motivational speeches that presented a contrast between what currently exists and what can be achieved (e.g. Martin Luther King “I have a dream”, Steve

153 Jobs Keynotes) and she had tried to reproduce this effect in her presentation. I considered that this supported my choice of rich points in this presentation.

The second one was a pilot analysis carried out simultaneously and independently by an external researcher and myself in one of the presentations, in this case a product pitch (see Appendix 3 PPSE1 and PPSE2). The two researchers viewed the presentation and independently selected two rich points using the same criterion: high persuasive effort from the speaker. Interestingly, we obtained the same results (i.e. the same rich points were selected). If two researchers select the same persuasive moments, this indicates that our perception of high persuasive efforts is not totally subjective.

Finally, my selection of rich points was cross-checked with the opinion of the three supervisors of this PhD and consensus was sought in (rare) cases of disagreement. For example, one of the rich points in a product pitch (see Appendix 3 PPKE2) was a dubious case. Two of the supervisors perceived as not particularly persuasive in comparison with other rich points already selected. After deliberation, we decided to keep it for two reasons: 1) it was still more persuasive that the rest of the presentation; 2) even if the persuasive effect was not fully achieved (neither of us were really convinced by the speaker), the persuasive effort could still be perceived (and was later confirmed by closer analysis), which is the real focus of this study. In other words, we considered that it could also be interesting to analyse an example in which persuasion is there, but it is not successfully conveyed.

The use of rich points was adopted to avoid prioritizing any semiotic mode in particular. I did not want to use one mode as the driver of the analysis and limit the analysis of the other modes to instances of co-expression (e.g. starting the analysis looking for lexical expressions of persuasion and then looking for correlates in the other modes). In this sense, the approach has proved useful to keep the focus on the multimodal ensemble as a whole (see section 1.1.4 in Chapter 1) and the way different modes interact to encode a persuasive message, as it will be discussed in detail in chapters 6 and 7. In using the rich point as a unit I side with Adolphs et al. (2011: 320) in their suggestion to analyse corpora including heterogeneous data, in particular with their claim that “the unit of analysis may shift from the word or sequence of words to a contextually defined episode of interaction which may include multiple modes of discourse and which is dynamic in nature”. I believe this definition of unit of analysis applies to the rich points I have defined in my study.

For the sake of building a comparable corpus, and as I mentioned in section 1.3.1 in Chapter 1, I excluded the Q&A session of all presentations in the selection of the rich points.

154 During this section a rather different communicative situation is built, entailing a different relationship between presenter and discussant. Because of this, previous research has actually considered it a closely related, but different, genre (Querol-Julián 2011; Räisänen 1999, 2002). Different communicative and persuasive strategies are, therefore, to be expected, which would further complicate the contrastive analysis of the genres. This will, however, constitute a fascinating topic for further research.

For the sake of feasibility, and in order to keep the corpus to a manageable size for multimodal analysis, I restricted the selection of rich points for fine-grained multimodal analysis to two per presentation, which gave a total of 30 rich points (ten per each of the three events). I did not try to make these rich points share the same length or position in the presentation, but in the two presentations that were co-presented I did select one rich point for each speaker on purpose. The total size of the conference presentations rich points is 7.2 minutes. The rich points in dissemination talks add up to 4.9 minutes and the rich points in product pitches have a length of 3.8 minutes. These differences in corpus size reflect the differences in the overall length of the presentations, conference presentations being considerably longer, followed by dissemination talks and finally product pitches which are the shortest. As will be explained in detail in Chapter 5, the results are presented in the form of a frequency per minute, thus facilitating the comparative analysis across presentations that differed considerably in length.

To keep track of the rich points while preserving the anonymity of speakers, rich points were coded according to the type of presentation, the initials of the speakers and then numbered (e.g. DTPI1 stands for Dissemination Talk, two first letters of speaker’s name and first rich point in the presentation). The transcripts of all of the rich points are compiled in Appendix 3. Table 5.5 offers an overview of the rich points in the three genres.

Table 5. 5 Rich Points subcorpus

CONFERENCE PRESENTATIONS

The role of the researcher in ethnographic investigations of workplace settings: Expert, consultant or confidante?