Turn design

Vocalizations as evaluative assessments in a novice partner dance workshop

I’m presenting with Dirk vom Lehn on a panel organized by two fantastic EM/CA scholars Richard Ogden and Leelo Keevalik on ‘non-lexical vocalizations’. We’re using some great video data we collected featuring novice dancers in a Swing Patrol ‘Dance in a Day’ workshop as part of the dance as interaction project.

CA studies of assessments as distinct, sequentially organized social actions (Pomerantz, 1984) have tended to define assessments for the purposes of data selection (Ogden, 2006, p. 1758) as “utterances that offer an evaluation of a referent with a clear valence” (Stivers & Rossano, 2010). However, this definition may exclude evaluative practices where the ‘valenced’ terms of assessment are more equivocal. It also obscures how the valences that mark out an utterance as an assessment are produced interactionally in the first place. This paper follows Goodwin & Goodwin’s (1992) proposal that assessment ‘segments’ (words like ‘good’ or ‘beautiful’), and assessment ‘signals’ (vocalizations like “mmm!” or “ugh!”) are organized into sequential ‘slots’ that render both ‘segments’ and ‘signals’ reflexively accountable as evaluative ‘assessment activities’. Data are drawn from recordings of a novice partner dance workshop at moments where teachers’ pro-forma terminal assessments marking the completion of a dance practice session co-occur with students’ evaluative assessment activities. Analysis shows how students use non-lexical vocalizations as evaluative assessments after imitating the bodily-vocal demonstrations (Keevalik, 2014) of the teachers and completing an unfamiliar dance move together. Extract 1 shows one example of these non-lexical vocalizations as dance partners Paul and Mary complete a new dance movement while the teachers call out rhythms and instructions.

Extract 1
(video: http://bit.ly/CADA_SP_03)


1 Tch1: tri:ple and ⌈rock step (0.8) BRINGING I::n. a::n rock step
2 Tch2:             ⌊rock step tri:ple an tri:ple a::n ro̲c̲k step
3 Tch1: tri:ple (.) tri:ple.≈
4 Mary: ≈⌈So̲rry. <(I’m a) little AUa:⁎U:h⁎ ((Shifts arm down Paul’s shoulder))
5 Tch2:  ⌊(a::nd then sto:p?)
6 Paul: Ye:: sHheh a:̲h⌈- yeh. (.) ∙HEh UhUH ->
7 Mary:               ⌊it⌈'s li- Au̲h- uh. ((Re-does and emphsizes arm-shift))
8 Tch1:                  ⌊ROTATE P::̲↑ARTne::::r::s::.
9        (0.8)
10 Mary:  ⌈Eya̲a̲::: ((Makes a clawing gesture))
11 Paul:  ⌊The bh- the bi̲:cep clench (°>dy'a know wha' I mean<°)≈ ->
12 Mary: ≈↑Y e̲a̲h̲h̲.⌈ it's- it's b- hh((Re-does and emphasizes clawing gesture))
13 Paul:          ⌊HAH hah Ha::h °hah hah° ∙HHh Heh heh ∙hh
14 Tch1: SO: WITH YOUR NE̲W̲ P:̲A̲R̲TNE:⌈:r.
15 Paul:                           ⌊That's an odd way of descri:bing it.

The analysis suggests that non-lexical vocalizations provide a useful resource for evaluating the achievement of as-yet-unfamiliar joint actions and managing and calibrating subtle degrees and dimensions of individual and mutual accountability for troubles encountered in learning a new, unfamiliar partner dance movement.

References

  • Goodwin, C., & Goodwin, M. H. (1992). Context, activity and participation. In P. Auer & A. D. Luzio, P. Auer & A. D. Luzio (Eds.), The contextualization of language (pp. 77–100). John Benjamins.
  • Keevallik, L. (2014). Turn organization and bodily-vocal demonstrations. Journal of Pragmatics, 65, 103–120.
  • Ogden, R. (2006). Phonetics and social action in agreements and disagreements. Journal of Pragmatics, 38(10), 1752–1775.
  • Pomerantz, A. (1984). Agreeing and disagreeing with assessments: Some features of preferred/dispreferred turn shapes. In J. M. Atkinson & J. Heritage, (Eds.), Structures of social action: Studies in conversation analysis (pp. 57–102). Cambridge: Cambridge University Press.
  • Stivers, T., & Rossano, F. (2010). Mobilizing Response. Research on Language & Social Interaction, 43(1), 3–31.

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Getting a backchannel in wordwise: using “big data” with CA

Here’s the abstract to an ICCA 2018 paper I’m working on with J.P. de Ruiter at the Human Interaction Lab at Tufts. The goal is to use computational linguistic methods (that often use the term ‘backchannel’) to see if all these responsive particles really belong in one big undifferentiated ‘bucket’.

Many studies of dialogue use the catch-all term ‘backchannel’ (Yngve ,1970) to refer to a wide range of utterances and behaviors as forms of listener-feedback in interaction. The use of this wide category ignores nearly half a century of research into the highly differentiated interactional functions of ‘continuers’ such as ‘uh huh’ or ‘wow’ (Schegloff, 1982, Goodwin, 1986), acknowledgement tokens such as ‘yeah’, ‘right’ or ‘okay’ (Jefferson, 1984; Beach, 1993) and change-of-state markers such as ‘oh’ or ‘nå’ (Heritage, 1984; Heinemann, 2017). These studies show how participants use responsive particles as fully-fledged, individuated, and distinctive words that do not belong in an undifferentiated functional class of ‘backchannels’ (Sorjonen, 2001). For this paper we use the Conversation Analytic British National Corpus (CABNC) (Albert, L. de Ruiter & J. P. de Ruiter, 2015) – a 4.2M word corpus featuring audio recordings of interaction from a wide variety of everyday settings that facilitates ‘crowdsourced’ incremental improvements and multi-annotator coding. We use Bayesian model comparison to evaluate the relative predictive performance of two competing models. In the first of these, all ‘backchannels’ imply the same amount of floor-yielding, while the second CA informed model assumes that different response tokens are more or less effective in ushering extended turns or sequences to a close. We argue that using large corpora together with statistical models can also identify candidate ‘deviant cases’, providing new angles and opportunities for ongoing detailed, inductive conversation analysis. We discuss the methodological implications of using “big data” with CA, and suggest key guidelines and common pitfalls for researchers using large corpora and statistical methods at the interface between CA and cognitive psychology (De Ruiter & Albert, 2017).

References (including references for the final talk – which has many more references than this abstract).

  • Albert, S., De Ruiter, L., & De Ruiter, J. P. (2015). The CABNC. Retrieved from https://saulalbert.github.io/CABNC/ 9/09/2017
  • Albert, S., & De Ruiter, J.P. (2018, in press), Ecological grounding in interaction research. Collabra: Psychology.
  • Beach, W. A. (1990). Searching for universal features of conversation. Research on Language &amp; Social Interaction, 24(1–4), 351–368.
  • Bolden, G. B. (2015). Transcribing as Research: ‘Manual’; Transcription and Conversation Analysis. Research on Language and Social Interaction, 48(3), 276–280. https://doi.org/10.1080/08351813.2015.1058603
  • de Ruiter, J. P., & Albert, S. (2017). An Appeal for a Methodological Fusion of Conversation Analysis and Experimental Psychology. Research on Language and Social Interaction, 50(1), 90–107. https://doi.org/10.1080/08351813.2017.1262050
  • Goodwin, C. (1986). Between and within: Alternative sequential treatments of continuers and assessments. Human Studies, 9(2), 205–217. https://doi.org/10.1007/BF00148127
  • Greiffenhagen, C., Mair, M., & Sharrock, W. (2011). From Methodology to Methodography: A Study of Qualitative and Quantitative Reasoning in Practice. Methodological Innovations Online, 6(3), 93–107. https://doi.org/10.4256/mio.2011.009
  • Hayashi, M., & Yoon, K. (2009). Negotiating boundaries in talk. Conversation Analysis: Comparative Perspectives, 27, 250.
  • Hepburn, A., & Bolden, G. B. (2017). Transcribing for social research. London: Sage.
  • Heritage, J. (1984). A change-of-state token and aspects of its sequential placement. In M. Atkinson & J. Heritage, M. Atkinson & J. Heritage (Eds.), Structures of social action: Studies in conversation analysis (pp. 299–345). Cambridge: Cambridge University Press.
  • Heritage, J. (1998). Oh-prefaced responses to inquiry. Language in Society, 27(3), 291–334. https://doi.org/10.1017/S0047404500019990
  • Heritage, J. (2002). Oh-prefaced responses to assessments: A method of modifying agreement/disagreement. In C. E. Ford, B. A. Fox, & S. A. Thompson, C. E. Ford, B. A. Fox, & S. A. Thompson (Eds.), The Language of Turn and Sequence (pp. 1–28). New York: Oxford University Press.
  • Hoey, E. M., & Kendrick, K. H. (2017). Conversation Analysis. In A. M. B. de Groot & P.Hagoort, A. M. B. de Groot & P.Hagoort (Eds.), Research Methods in Psycholinguistics: A Practical Guide (pp. 151–173). Hoboken, NJ: WileyBlackwell.
  • Housley, W., Procter, R., Edwards, A., Burnap, P., Williams, M., Sloan, L., … Greenhill, A. (2014). Big and broad social data and the sociological imagination: A collaborative response. Big Data &amp; Society, 1(2). https://doi.org/10.1177/2053951714545135
  • Jefferson, G. (1981). On the Articulation of Topic in Conversation. Final Report. London: Social Science Research Council.
  • Jefferson, G. (1984). Notes on a systematic Deployment of the Acknowledgement tokens ’Yeah’ and ’Mmhm’. Papers in Linguistics, 17(2), 197–216. https://doi.org/10.1080/08351818409389201
  • Kendrick, K. H. (2017). Using Conversation Analysis in the Lab. Research on Language and Social Interaction , 1–11. https://doi.org/10.1080/08351813.2017.1267911
  • MacWhinney, B. (1992). The CHILDES project: Tools for analyzing talk. Child Language Teaching and Therapy, (2000).
  • Nishizaka, A. (2015). Facts and Normative Connections: Two Different Worldviews. Research on Language and Social Interaction, 48(1), 26–31. https://doi.org/10.1080/08351813.2015.993840
  • Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600–2606. https://doi.org/10.1073/pnas.1708274114
  • Ochs, E. (1979). Transcription as theory. In E. Ochs & B. B. Schieffelin, E. Ochs & B. B. Schieffelin (Eds.), Developmental pragmatics (pp. 43–72). New York: Academic Press.
  • Potter, J., & te Molder, H. (2005). Talking cognition: Mapping and making the terrain. In J. Potter & D. Edwards, J. Potter & D. Edwards (Eds.), Conversation and cognition (pp. 1–54).
  • Sacks, H. (1963). Sociological description. Berkeley Journal of Sociology, 1–16.
  • Schegloff, E. A. (1982). Discourse as an interactional achievement: Some uses of ?uh huh?and other things that come between sentences. In D. Tannen, D. Tannen (Ed.), Analyzing discourse: Text and talk (pp. 71–93). Georgetown University Press.
  • Schegloff, E. A. (2007). Sequence organization in interaction: Volume 1: A primer in conversation analysis. Cambridge: Cambridge University Press.
  • Steensig, J., & Heinemann, T. (2015). Opening Up Codings? Research on Language and Social Interaction, 48(1), 20–25. https://doi.org/10.1080/08351813.2015.993838
  • Stivers, T. (2015). Coding Social Interaction: A Heretical Approach in Conversation Analysis? Research on Language and Social Interaction, 48(1), 1–19. https://doi.org/10.1080/08351813.2015.993837
  • Rühlemann (2017). Integrating Corpus-Linguistic and Conversation-Analytic Transcription in XML: The Case of Backchannels and Overlap in Storytelling Interaction. Corpus Pragmatics, 1(3), 201–232.
  • Rühlemann, C., & Gee, M. (2018). Conversation Analysis and the XML method. Gesprächsforschung–Online-Zeitschrift Zur Verbalen Interaktion, 18.
  • Wittenburg, P., Brugman, H., Russel, A., Klassmann, A., & Sloetjes, H. (2006). ELAN: a professional framework for multimodality research. In 5th International Conference on Language Resources and Evaluation (LREC 2006) (pp. 1556–1559).
  • Yngve, V. (1970). On getting a word in edgewise. Chicago Linguistics Society, 6th Meeting, 566–579. Retrieved from http://ci.nii.ac.jp/naid/10009705656/

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