Inclusive data session guidelines

Source: https://darg.lboro.ac.uk/inclusive-data-session-guidelines/ Parent: https://darg.lboro.ac.uk/

Latest update: 17.9.2024

This page is based on the participation and facilitation guidelines written by the EMCA4RJ community. We aim to live up to these guidelines in our DARG data sessions. Please always feel free to contact dargcoordinator@lboro.ac.uk with any issues/questions about your experiences at our weekly DARG.

Values

To create a welcoming, supportive, accessible environment for all
To explore, critique, learn and exercise analytic practices
To socialize and develop the community of EMCA and other language and social interaction scholars
To interrogate presupposed assumptions about language use
To cultivate a love of the analysis of social action and language use

Dilemmas and suggestions


Components of Data Sessions	Some Dilemmas in Accomplishing Values		Some Suggestions
*CONTEXT*	Ignoring any form of ethnographic context could suppress people’s contributions that draw on it, or unnaturally reduce analysts’ access to what forms the basis of participants’ understandings	Alternatively, here-and-now is an important CA method to learn, and can help analysts learn to “toggle” between assumptions and interpretations	Be explicit about how ethnographic context is being used; develop connecting the empirical record to an ethnographic record in careful ways
*DATA*	Focusing on classic or homogenous data that may encourage participants to see a certain kind of person as the default or universal basis of analysis (e.g. white participants)	However, not sharing classic data can be problematic, such that because it means that certain references/shared knowledge are inaccessible to analysts who have not been exposed to that data	Diversify data; share but also contextualize/critique the basis of examining classic or homogenous data
*PARTICIPANTS (within the data)*	Value judgements, for example, remarks about a participants’ personality or competence. Such comments. Such comments can come across as disrespectful of the people featured in the data and sometimes border on reproducing stereotypes about social categories.	However, criticizing a participant’s displayed position or action may be important in some circumstances (e.g., when they are observably exercising forms of oppression). Such criticism should still be handled with care, and not overwhelm the analytic nature of the session	Be explicit about the basis for expressing a judgment. Grant the people in the data fundamental consideration and respect
*TRANSCRIPTS*	Demanding transcription style or detail can shut out those still developing their skills or imply problematic “objectivity” expectations	It’s also the case that we don’t want to dismiss detail a priori or ignore elements (like laughter production) that could be relevant	Have a clear expectation for how and why data is transcribed as it is, and what the presenter wants out of the data session experience
*ANALYSIS*	Shutting down certain lines of analysis; insisting that some line of analysis cannot be done or is inappropriate within the method	But also, not being sufficiently open with beginners about how their analysis will be seen outside the data session by scholars who do	Start with examining how we interpret or how we’ve come to a certain analysis rather than suggesting some interpretations are off limits
*PARTICIPATION*	Letting advanced scholars set the agenda, do policing, have most of the voice in the discussion	However, no presence from established analysts may do a disservice to developing analysis (and the mix of skills is often part of the purpose of a data session)	Mix of people of different analytic skills and experience; emphasis on guidance; not showing off or testing people; valuing different skills (e.g., newcomers can see things established people may not)
*PROCESS*	Self-selection can end with a handful of people having the floor and less opportunities for others; can feel more like a seminar than a collaborative enterprise, with certain people dominating	Despite this, opposite everyone-must-speak practices can put a lot of pressure on individuals or lead to comparison (possibly competition); feels like a performance	Be attentive to the perspective of the particular group on this; mix methods according to the purpose of the data session (see below)
*PURPOSE*	Building and honing core skills can make newcomers anxious and preclude more exploratory conversations about the data	On the other hand, where people want training or specific outcomes in the session, too much exploration or breadth of views can provide a too-abstract experience	Have a mix of different types of data session; the purpose should be clear, and the other aspects listed in the rows above should be suited to the agreed-upon purpose
*CONTENT ALERTS*			Please alert attendees about any sensitive contents in the data – both when advertising the session and at the beginning. Our University provides guidance on sensitive contents: see section 3.9 here

Process

These days, we usually run DARG data sessions online and hybrid, so there can be quite a few people (20+) online, as well as the 8-12 we usually have in the room. To ensure that everyone gets an opportunity to speak, but without singling people out, we have developed the following protocol:

We do a name-round, where everyone gets to introduce themselves in a few words
The presenter introduces themselves and their data, playing the audio/video several times
Before going round the room for initial thoughts on the data, we ask everyone to observe a few house rules.
The initial round is for sharing a specific observation about a particular line or section.
We have limited time, so stick to making one observation. We can expand on it later.
It is tempting to launch into a full analysis, but we will ask people to stop so everybody gets a turn.
We suggest giving a line number and making an observation of about one or two sentences.
If someone has already mentioned a specific section or line you wished to focus on, you can add a further observation about it, or pass.