blogging research projects

I am often asked about the ways in which I use blogs for research purposes. I take this question  to mean I should talk about something other than the usual blogging that I do. So here goes.

I have played around with various research-related blogging strategies. Here are two that seem to have worked fairly well for me and colleagues:

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  • Blogging a serial-style literature review

Research partners are invited to tune in each week to a shortish post which is one section of a literature review. Over time, the full literature review is posted.

An example – the performing impact project looked at the ways in which community theatre companies might learn more about the impact they had on participants. We invited a small group of community theatre practitioners to join us in thinking about how formative evaluation might be a useful way to do this. Our first step was to generate ideas from the literature which might be used for thinking about the various stages of developing a play and a performance. We blogged these ideas each week and suggested that our theatre partners might like to read some of the posts before they came to a workshop. They did. And while no one had read all of the posts, everyone had selected those posts which seemed most interesting to them. We discussed their chosen ideas in the first part of a two-day workshop, before going on to think about the practicalities of actual evaluation.

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  • Blogging appreciative stories

An example – a current project, TALE, funded by Arts Council England, is a three-year study of teachers and students engaged in performing and visual arts. Our partners are the Royal Shakespeare Company Education team (who direct the project) and Tate Schools and Teachers team. As we visit our thirty schools we write a short impressionistic post about an aspect of the visit. These snapshots are intended to give an idea of the interesting practice going on in each school in and through the arts. We know that our funder finds these posts interesting, as do the schools involved. There are also some other arts researchers and school practitioners who read the posts.

It’s important to note that we did not set out to reach a wide audience using these research blogs. But both examples do have a very clear sense of audience and purpose. They both aim to engage partners involved in the research, plus an immediate and small audience beyond. The posts are frequent enough to be anticipated, perhaps be a novelty – and not so far apart that our partners forget about them.

Each of our two blog types is tightly focused and the posts are also similarly narrow in content. Each performing impact post for instance addressed one idea in the literatures. Each TALE post describes a particular school and an event, conversation and/or practice.

The performing impact project blog also now contains a report of the final workshop and, as the project has now well and truly ended, it has become an open access archive. The site still gets a few visitors each week; visitors seem to find the posts germane to their interests, as they usually look at more than one post and page. Our quality in alternative education research project took  a similar approach – we posted a literature review and case studies – and it gets similar continued, but minor, ongoing traffic.

These two kinds of blogs have modest aims. They aim in the first instance to support the community engaged in the research itself, rather than a more general public. They are an ongoing means of communication about progress. They do what we might once have tried to do – less successfully in my experience – through a project newsletter. The posts are intended to create interest as well as share some initial ideas with the research project community. The blogs are also a form of accountability to the participants, and to the funder – they can see what we are doing and where.

But we are/were also clear about what these kinds of blogs are not. The two examples I’ve given were not research blogs concerned with organisational questions about who, what and when – we manage logistical concerns via email and telephone. Nor were they to garner public interest. Some research blogs do aim to publicise events, but ours weren’t/aren’t those kinds of projects. We weren’t/aren’t concerned to build up a huge network or to inform public opinion during the research – that comes later in the case of TALE.

The key questions we asked of ourselves as we established these research blogs were:

  • What specific community do we want to engage?
  • What aspects of the research do we want them to know about?
  • What aspects of the research will they be most interested in?
  • What format is most appropriate?
  • How frequently do we need to post?
  • What off line conversations do we want to support via the blog?
  • How will we let our community know about the posts when they publish?
  • How will we tell other interested people that the blog exists?

We do the obvious things to let people know about the research blogs – we tweet about each post, and we notify our friends and colleagues via facebook. We put the links on our email signatures, and publicise posts through our university twitter accounts and on our university home pages and school websites. Our partners also tweet and make the links available on their own websites.

The archive function of research blogs is also important. While there is a lot of fretting about personal blogs becoming dormant, completed research blogs are more like any other completed research project – their reports are available as a resource for those interested in the topic.

Sometimes archived projects are useful in other funding applications and in related work – they act as evidence of previous activity that is being built on. Our GetWet website for example is an archive of two-year action research project: it stems from a long-term partnership with a local museum. Other subsequent water and museum projects always refer back to this original archived activity. Ditto our research on artists working in schools, the  Signature Pedagogies project.

And, it has to be said, as we start to put together an impact case study (REF, UK) for our overall arts education research, the combined ‘effects’ of all of the research websites and blogs come into play. While a blog or website is not in itself an impact, it can be an important step along the way.

But research projects always lead to other forms of publication produced in addition to blogs. And when the books and papers and more mainstream media articles appear, the blogs then become a kind of multi-media appendix which can be explored by interested readers. 

 

 

Posted in academic blogging, archive, blogging about blogging, literature review, research, research agenda, research blogging | Tagged , , , , | 2 Comments

viva? conference presentation? it’s all about the ‘improv’

So it’s one of those academic occasions when you have to present yourself and your work  – to people who are there to judge you. Think the viva. The interview panel. The first encounter with a new class. The conference presentation to an unfamiliar audience.  A high stakes occasion. Scary.  When you present yourself and your work at the same time, you and your work become one – a performance of scholar, scholarship and scholarly work all wrapped up into one make-or-break event.

There’s often a whole lot of anticipatory shakin’ goin’ on before such occasions. And with very good reason. We’ve all heard stories about the perfectly capable person who flubbed their interview presentation to staff and didn’t get the vote or the job. Or the recognised expert whose conference paper was incomprehensible because they were so wound up.  Or the viva where the person could hardly manage a word.  We may even have witnessed some of these occasions – or worse still have actually had the experience ourselves, and if so, it’s one we never want to repeat.

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Improvisation – Eric Decossaux. Not a viva…

But hang on. Nerves are not just something that happens to other people. The reality is that most of us have had at least one academic performance experience we’d rather not remember. Apart from anything else, it’s hard to be on point all the time. Anyone who does a lot of public speaking will tell you that sometimes things just don’t go as well as they might.

It ought to be comforting to know that you’re not alone. But much of the time, the occasions when other people marginally flop don’t usually seem as bad as the one where it really matters if you do … The viva. The interview panel. The first encounter with a new class.  Shudder. The problem with these occasions-that-matter-more-than-most is that they require a tricky combination of knowing your stuff, managing the technical details and giving a performance of a particular kind of ‘self’ all at the same time.

There’s a lot of helpful advice out there about some of these kinds of situations, combined with personal testimony and handy hints. But a lot of the advice doesn’t quite deal with the particular combination of  content, process and a ‘you’ that are at stake. Just take the viva as an example.

Content

By the time, you hand in your thesis you know your stuff pretty well. In fact, for a short time after you hand in you can probably just about recite particular pages by heart. This accuracy dulls after a while. But of course you can always read the text again before the viva, just to refresh your memory. And you do.

And knowing your stuff  is the one bit of the viva that you are often told you can feel most sure about. Really? I’m not so sure.

The rub is that the viva is the very first time you hear what someone other than your supervisors and your mates have to say about three years or more of your life’s work.  Even if you do know your stuff back to front and inside out, you still don’t know what the examiners  are going to say. Because knowing your material is one thing – and knowing how it will be seen by examiners is another. Feeling nervous is a perfectly logical and sensible response to this kind of uncertainty.

And you could say the same thing about a conference presentation or an interview. You can know your material VERY well, but you don’t know how it will be received.

Process

Most people do preparatory work for the viva – thinking of the questions they might get asked. Prep work can be very helpful, although in my experience examiners often ask things that aren’t on those lists of common viva questions. But the process of the viva isn’t just questions and pre-rehearsed answers to predictable questions. It’s also what you aren’t expecting. It’s about things you haven’t prepared for. So it’s not very sensible to feel completely serene about having a lot of prepared answers to predictable questions.  You will need to compose an answer on the spot – one that’ s not too long and not too short, making sure that you don’t lose track half-way through.

And yes, it can be helpful to think about techniques and props to assist you to extemporise. Write notes to self. Drink water. Pause and repeat the question. All good stuff. It’s OK to have some kind of prepared actions to fall back on if you need to. And of course, you can ask for clarification if you don’t understand. You can’t do that with every question though or you look silly. So, you can ask, but not too much.

But regardless of all the props you can carry and muster at the time, you still need to talk and at the same time think about how you are doing in the presentation and whether you need to modify or change what you are doing. You have to monitor your own performance as you are performing.

The viva is not just about one you’ve prepared earlier. It’s also about improvisation, thinking on your feet. It’s about listening to a question, translating it in your head, and then providing a succinct answer.

Now a lot of people swear by mock vivas. I agree that they can be useful. But I think there is a clear and present danger that some people see mock vivas as a means of practising a script, talking about what they think are likely to be the actual questions that they will be asked. I think that’s dangerous – see above. A better use for a mock viva is as a rehearsal in how you might improvise and self-monitor, remember what you know, think about how you are coming across – all at the same time. It’s an occasion to see what this tricky process is like, and how you might manage to extemporise in a convincing way about your work.

There’s an equivalent in conference and class presentation too –  although scripted practice is a more secure approach here as you do have a longer time to do the pre-prepared material. But there’still comes a point where it’s improv.

The performing scholarly you

The tricky bit of vivas, conferences is where you have to think about how to project a combination of comfortable, confident and chutzpah. There is something helpful to be said for thinking about this as a performance, and thinking about what can be learnt from performance practice.

Actors know that they must deal with nerves and don’t worry if they experience them. It’s part and parcel of their game. They know that they must be well prepared, learn their lines, and even if they forget them must carry on as if nothing is wrong. This is a not unhelpful attitude for high stakes scholarly performances too. As long as there are occasions when we have to get up on our feet and project ourselves and our work into a scary context we have to find ways to deal with the moment by moment self-aware actions that we take.

It’s not all that silly to consider what you will wear, how you will actually speak (lexicon, tone, volume), the props you might want to take in with you, how you will make connections with your audience… and perhaps study those who embody and perform the kind of scholar that you want to be. But, you still always need to anticipate a random left of centre question from the examiner/audience, and counter the effect that nerves might have on your capacity to respond … 

Now I know the idea of performance goes against the grain for many people who prize feeling ‘authentic’. And for those of us of a more critical bent, focusing on a performance can feel a bit like a game you don’t want to play, too performative altogether.  But on the other hand, games and performances do require a kind of analysis of rules and expectations and fashioning a show for a particular occasion – this is about playing the game to win when it counts most.

And the more you do improv – you rehearse for an improvised performance – the more you come to understand what’s involved. And the more you get used to working through and with the nerves. That doesnt mean it gets easier – just more familiar.

I’m interested in ways that people have found to think about these kinds of sweaty tricky performative occasions … how do you manage the embodied and frequently anxiety-inducing combination of self-critical and self-aware performance of your scholarship and you the scholar?

 

P. S. I have noticed a couple of universities where theatre practitioners offer workshops to help staff and doctoral researchers learnt improvisation and rehearsal techniques. Onya, I say.  Way to go.

Posted in conference, conference presentation, conference questions, conference survival tips, improvisation, rehearsal, Uncategorized, viva | Tagged , , , , | 6 Comments

beginning data analysis – orienting yourself

This post is a response to a question about how to begin data analysis.

When you were little, I bet you played sorting games. You might have organised pencils into colours, or blocks into various shapes. Later on, you may well have sorted those same blocks into sizes as well as shapes, perhaps even added their material into the mix. You probably also transferred pattern-making into paper and pencil activities.  

Maths teachers understand this kind of pattern-making to be early mathematical thinking.

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However, sorting games do more than provide a foundation for number. Sorting and categorising are thought processes that we use to understand the world in general – and in particular, they are the very processes we use when we are analysing  data – sorting and categorising the ‘stuff’ we have generated. 

We look to make patterns and establish commonalities in data by finding connections and associations. And we develop categories for our chosen patterns and groupings   – the equivalent of children’s sorting of red and yellow pencils  – and categories for the overall exercise – the equivalent of the child’s “Look Mum, I’ve done a big pencil sort”. So by the time we are adults we have internalised ways we orient ourselves to pattern-finding/making. 

It’s not unhelpful for we researchers to make these tacit orientations to pattern-making more explicit, so we can see if they are up to the job of data analysis, and to see which strategies are good for what tasks. 

If you want to do just this , you might like to check your orienting pattern-making strategies against these sentence starters – just for starters. (Note – these sentence starters are geared to qual data, but many of them also apply to quant.)

When we are finding commonalities, we generally say things like…

  • This is similar to… because
  • This is different from… because
  • This relates to… because

We often then find ourselves refining our ideas of what our data might allow us to see – and we begin to look for particular things…

  • What if…
  • This raises the question of….
  • What is meant by…
  • How is it possible that…
  • I wonder why…

And we begin to make some initial inferences from what we see in the data…

  • It seems as if…
  • I’m guessing that…
  • I’d expect to see more of …
  • If this… then probably also…

In quants research, we statistically test out these initial ideas and inferences, while in qual data we look for additional confirming or disconfirming material. In both traditions, frequency and/or quantity of the ‘this’ is important for a pattern to hold fast.

And then we begin to generate overarching analytic categories

  • This is primarily about…

And evaluative summaries

  • The most important things here are…

We often look for associations and categories using our pre-existing scholarly knowledge – in research this is usually from the literatures.

  • This is connected with the literature on a … because …
  • This also appears in b and there it is about…
  • This reminds me of c which…

As we get on with association-making, we generally begin to think about explanations for the patterns we are seeing.

  • Why is it like this?

Of course, we may find ourselves going back over the data and our analysis as we think…

  • This doesn’t seem to fit. Why is that?
  • I am confused about…
  • I couldn’t quite grasp …
  • I need to check back about…

But. But there are also things we are less likely to do/ask ourselves when we start grappling with data.

Unless we are doing practitioner research, practice research or a professional doctorate, where the use of prior non-scholarly knowledge is important, we usually don’t allow ourselves to dwell on associations such as…

  • This happened to me when…
  • When I experienced this I…
  • The usual response/reason is…
  • Before I have…

And unless we are doing ethnography or some kind of research in which our own affective responses are important, then we also don’t often think…

  • I can picture…
  • This made me feel…
  • I now imagine…

Some people, and I am one of them, would suggest that both our prior knowledge and experience and our emotional responses are always involved in pattern making. They/we would argue that it’s better to acknowledge these influences, and see what lurking ideas and feelings are producing our researcher-self pattern-making – recognition that you can ‘see’ your own taken-for-granted ideas.

And a benefit. You can also sometimes find surprising clues about new patterns and new ways to write about them when you look at your experiences and emotions. Ask yourself for instance – what data made me feel sad – and see if any new associations and inferences occur.

 

Image: Studio Alijn

 

Posted in data, data analysis, orienting questions, qualitative data, Uncategorized | Tagged , , , , | 2 Comments

play with your data

Data analysis can be pretty scary.  That moment when you realise that making sense of the stuff you’ve so painstakingly generated comes down to you – just you.  Well, relax. It’s not just you that has to leap into the unknown. We all put ourselves on the line when we make decisions about definitions, scales, codes, themes – and when we saywhat the analysis eventually means. But that realisation can be alarming – maybe it’s even paralysing.

You might think that qualitative researchers suffer most from nervousness about interpretation –  facing up to those mountains of transcripts and/or field-notes and/or photographs can be really daunting.  But of course surveys also often contain open questions too so  mixed methods and quants researchers also often face lots of words, not to mention the task of actually converting numbers into something convincing and believable.

Doctoral researchers often get the worries at the beginning stages of data analysis. What if I am just being stupid? What if I am barking up the wrong tree altogether? What kind of criticisms might be levelled at the choices I have made? 

So it’s not at all surprising that in the face of masses of data that doctoral researchers opt for a technique-led approach to analysis. After all, regardless of whether we are doctoral or highly experienced researchers, we all have to demonstrate that we have approached the processes of data analysis systematically, and consistently.

But sometimes the need to be technically proficient is more like clutching at a life raft. If I just do the analysis ‘the right way’ then that’s OK. Showing I can follow a set of ‘rules’ is all that matters. I’m not vulnerable because my exemplary technical prowess is all that counts.

We researchers quite rightly try so hard to get rid of anything that might seem dodgy. For example, one of the strategies that people employ to guard against idiosyncratic individual judgements is ‘inter-rater reliability’. This is where two people code the same transcript. An interpretation is reliable if both people agree – if they ‘see’ the same thing.

But isn’t that also a worry? If we are looking at two people with the same sets of understandings because they are in the same disciplines and have had the same kind of research training, then something else might be going on. In finding a common account of and for their data, they might be just acting really predictably and conventionally. They might be simply finding what they expect to find. Processes of choice and interpretation of data are precisely where taken-for-granted ways of thinking can really get in the way. You see this pattern, you opt for this direction because it just ‘seems right’ or ‘obvious’ or ‘expected’.

But what if it’s not so straightforward?

I like to occasionally stand the safety of data analysis technique and the cosy togetherness of being ‘right’ on their orthodox little heads. What if the discomfort with choice and uncertainty is actually helpful?

The grey area of researcher decision-making is where things get most interesting. It’s where you get to be most creative. And it’s where you can build a set of practices that help you step momentarily away from the usual and purely technical ways of doing data analysis.

I reckon it’s good to make time, when getting to grips with your data, to mix it up. Perhaps after you have done an initial analysis. Not right at the start. Get into the material as you usually would. Find the narratives in the interview, the codes in the transcript. Whatever. Then PLAY. Yes play.

But not any old play. Data play. Play that is designed to help you see new ways of connecting, new patterns, new groups, new associations, new commonalities, new aspects of context. In your data. Get out of the rut of the anticipated. Get rid of the grip of the immediately obvious.

And to that end here’s a few suggestions for data–play which does just that.

  • Random associations

Write each theme you have identified on a post-it note. Put all the post-its face side down, as you would with scrabble tiles. Now pick up three. Stick them to a large sheet of paper so you make a large triangle. Draw a line which connects them all. Now write what that line means – what connects them. The ask yourself – What do these three things have in common? Write this in the middle of the triangle.

You can keep doing this exercise as long as its generative. And you can vary the number of postits too  – four, six, ten, whatever you can manage. You do have to keep replacing the post-its that you’ve stuck to the paper of course, but remember that the object is for the set that you pick up to be random. Not planned.

You can also substitute what goes on the post-its too. You might put down ideas that you are currently working with.

A further variation is to work with data and possible theoretical explanations you might employ.  Make two piles of different coloured post-its with one for ideas or themes, and  the other for theoretical concepts. Now you simply pick up two or three themes or ideas and one theory post-it, stick them on a paper and see what lines and connections you can make.

  • Scatter gun

This is a variation on random associations. Cut all of your themes up into individual strips. Throw them down onto a large sheet of paper. You now have the categories for a random mind map.

Now draw the associations between the post-its  and connections – what links to what and why? What larger concepts are you working with as you make these connections? Are there associations that you haven’t seen before?

  • Redactions

Take a page from an interview transcript and a marker. Cross out all of the words that don’t immediately strike you as interesting. When you have finished, you will have a combination of phrases and individual words.

If this is an A4 sheet of paper, it may help at this point to enlarge it to A3.

Now pin the sheet on the wall and read aloud the words that are visible. What strikes you as you listen to yourself reading? Next, look for repetitions and patterns among the words and phrases that remain on the page.

You can do this exercise with entire transcripts. You can also compare random redacted sheets from similar interviewees to see what putting their visible words next to each other shows you.

  • Side by side

If you are using images in your study then switching from thinking about them as illustrations to actual data can be a bit tricky. One way to help make this adjustment is to select three or four images that particularly strike you – they seem to ‘say something’ about your research participants or the place you are researching. Pin the images up on a wall next to each other – or peg them on a clothes line – or lean them up against a wall – until you get what intuitively appears to be a pleasing sequence.

Now think about what holds these images together and why they are better displayed in this particular order.

You can also think about what joins individual images together and what sits in between them. Imagine that these images are the only things that you had to understand your research participants or place – what do you look for in the image that might give you information or a feeling about them?

I’m sure you can think of other ways to do serious data play.

As I’ve already suggested, it is worth putting aside a little time to get playful with data, as it can help you to break out of predictable sets of associating and patterning. And data play isn’t a substituted for more conventional and systematic data analysis – but it is a complementary practice, and a sometimes very necessary disruption.

And it’s great to do something playful like this in teams as these approaches generate great conversations.

 

Image – Ambiguous Cylinder Illusion

 

 

 

Posted in data, data analysis, play, qualitative data, Uncategorized | Tagged , , , | 2 Comments

a thesis writing-feedback calendar

How does a thesis get written? What do I as a supervisor do to help? How does feedback work best? A set of inter-related questions that keep many of us mildly, or a lot, worried. 

Well, I have an ‘ideal model’ for feedback on a thesis. I don’t always follow it. Quite often my model doesn’t work in the way I imagine it could and should, because either I or the doctoral researcher, or both of us, haven’t set aside enough time.

But it might be helpful for me – and maybe for PhDers out there – to do a bit of show and tell about my ‘magical feedback calendar thinking’. It’s really just as an example, not a best way. But because I don’t see feedback and calendars talked about very much I thought it might be interesting to put it out there.

A caveat – I’m talking here from a Social Science-Humanities perspective, and about a Big Book thesis. What happens in a lab-based project, or a PhD by publication, will differ quite significantly from what I am about to say. And lots of supervisors in my disciplinary fields won’t share my ideas either. That’s OK. The general point I’m making – that it’s helpful for supervisor and doctoral researcher to develop a calendar for writing and feedback – is applicable to everyone.

So to my little imaginary… What I most like to see by way of thesis writing and feedback are progress through these five stages:

  1. Written chunks of data analysis.

Writing chunks of analysis allows the doc researcher and I to discuss the ideas they are working with, potential arguments, theoretical framings and possible structures for the text. This kind of chunky writing can begin way, way before field work is finished.

2. Thesis abstract and brief abstracts for each chapter.

Writing the overall abstract  and the thesis structure affords discussion of the research contribution and the way it will be set up in the thesis. How will the introduction create the warrant for the research? What will the thesis argue? How will this argument be expressed as  major ‘moves’? This discussion leads naturally into considering how the argument will be choreographed as text – How will the data analysis and discussion be organised into chapters? How will the literatures be presented – separately or together? How and where will any theoretical framing be presented? What will the conclusion emphasise?

There is usually at this stage some writing of individual chapters. Draft chapter writing focuses on the mini-argument made in each separate chapter. Is the chapter argument persuasive? What is included in the chapter that ought to be somewhere else in the thesis? What is excluded from the chapter that needs to be in? What literatures are being cited, and are they enough and are they up to the job? Some preliminary feedback on writing might well happen here too – so common problems such as literature-as- laundry-list can be addressed earlier rather than later.

3. First whole draft.

Feedback on a first draft focuses on the quality of the ideas and the flow of the argument. Does the argument hold up? Are obvious objections and issues deal with? Are there any over-generalisations, false claims, under-assertions? Is the engagement with literatures and the analysis suitably critical and evaluative? I also have other things in my head at this point too. Should things that are currently in one chapter be in another? Are any obvious things left out? What might go in an Appendix? How do headings and sub-headings carry the argument? Are there any obvious writing tics and problems that can be addressed now rather than later? Does the writer have a ‘voice? Are there any stylistic tools that might help the text have more ‘life’ and ‘character’?

4. The second draft.

The fine-grained examiner-hat-on feedback. This is the most time-consuming stage for me, as it involves detailed attention to paragraphing, syntax, choice of terminology, phrasing and so on. While I continue to look at the argument and analytic sophistication, I also check to make sure the references that I think the examiner will want to see are cited. I also check the style of the referencing at this point, looking for consistency. This reading means I do lots of track changes and I need a lot of intensive time at the computer. At best, I manage about two chapters a day of this kind of work.

Sometimes stage ( 3) has to be repeated in order to get the text working before going on to (4). Because of time, a first draft may combine elements of both (1) and (2).  Even (3) might be folded in under worst circumstances. Combinations of feedback stages can be pretty overwhelming, because both substantive content and form are under the supervisory lens. (See bleeding thesis.)

5. The last run-through.

This is not a proof read (that’s the doctoral researcher’s responsibility)  – although if a good proof read is needed it will be obvious at this point in time. Rather it’s a final check on the argument, warrant and conclusion and, most importantly, catching up with the revisions resulting from the fine-grained read. A tracked changes final draft is therefore time-saving for both doctoral researcher and me.

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Now these five stages may equate to more than a twelve-month work-plan. More days than in a whole year. 365 plus days… Scary when you think of it like that. Not only for the doctoral researcher – but also for the supervisor, particularly if there are more than one or two people on the same completion run-down time-table as I have now.

But seeing thesis development as a set of stages – even if yours are vastly different from mine – is good. It’s not only useful as a way for doc researchers to work out what to do when and what to expect by way of feedback. It’s also a guide to developing writing-feedback calendar too. Counting back from submission to the very beginning of chunks of data and putting weeks and months against them provides a real sense of the way ahead. While it might also be daunting, getting your head around the path to completion is also integral to feeling – and staying – in control. And of course, it’s absolutely critical to be realistic about how many weeks it’ll take to write the text for each sequence – and how many weeks it’ll take me to do the feedback. Needless to say, if circumstances change, or if a repeat stage is required, then the calendar has to be adjusted.

The calendar can also help PhDers to sort out where conferences fit in, what to do in between drafts while waiting for feedback and when might be good times to do some writing for publication.

But the calendar is also important for supervision. It helps me enormously if I can also plan my year around the times when I know I will be doing intensive reading and feedback of thesis work. My own book writing depends on finding times when I don’t have to do a lot of feedback work.

Now as I said already, but let me repeat, I don’t expect that all supervisors will have the same way of doing things that I do. Your supervisor may prefer that you work together on all of the chapters one by one so that they are all written to a pretty good standard – then only one draft will need and get feedback. A lot of supervisors don’t work with abstracts like I do. Your supervisor will probably have some other sequence of writing and feedback that works for them and your topic.

But feedback and calendar are a Good Thing to talk about. Setting up a writing-feedback  calendar provides a VERY helpful focus for a discussion sometime towards the end of field, library or lab work.

And it’s a good thing to pin your calendar on your wall above your screen next to your research question. You get to cross off each stage as you finish them – so you see yourself creeping up on completion. Yay. 

Just in case it’s of use, here’s my ideal writing feedback table, just for interest you know… you do your own version. Then make it into a calendar with actual real-life dates. 

TARGET COMPLETION DATE

FINAL DRAFT Date handed into supervisor Date handed back
FINE-GRAINED READ Date handed into supervisor Date handed back
FIRST DRAFT Date handed into supervisor Date handed back
CHAPTERS

1.

2.

3 etc

Dates handed into supervisor Dates handed back
ABSTRACT AND CHAPTER OUTLINES Dates handed into supervisor Dates handed back
CHUNKS OF ANALYTIC WRITING

1.

2.

3. etc

Dates handed into supervisor Dates handed back

 

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social media? sometimes it’s just nasty as ****

 

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Like many of you I’m sure, I’ve been watching the extremely ugly and misogynist twitter assault on Mary Beard over the last week or so. Like many of you I’m sure, this has made me angry, sad and more than a bit worried. It’s a manifestation of a blood sport approach to debate that Mary, and multitudes of the rest of us, deplore.

The abusive combative exchange not only makes me feel ill, but also a bit despairing at the lack of change in academic cultures …. let me explain.

When I was a young person, thinking about whether to pursue an academic career or not, there was a notable propensity for some academics to verbally pulverise anyone who disagreed with them. I have a very vivid memory of a Masters’ tutorial where the topic was whether Stanley Livingstone was an imperialist or not. The tutor, a senior academic, took great umbrage at anyone who wanted to answer in the affirmative and shouted, berated and belittled, effectively silencing everyone. He relied on seniority, bluster and fear to make the case that if someone didn’t think they were an imperialist then they weren’t. (Yes, it was a pretty untenable argument and maybe that’s why he felt he had to defend it so vigorously. And yes, the gender politics were also what you might have predicted. )

This wasn’t the first time he’d yelled and carried on like this – but it was a last for me. I hated it. Everyone in the Department knew about it and no-one did anything. So why sign up for a career where hierarchical intimidation was accepted? I decided then and there that if that’s how the academy conducted itself I wanted no part of it. Instead I went to work with young people who’d been kicked out of school – many of them were young offenders.  They were often very friendly and nice to be around, and if they weren’t, at least there was a good reason for their mean-ness and outbursts.

Coming back into the academy much much older, and at a different time, I was at first relieved to see that academics had largely left insufferable ad hominen brutality behind. Conference behaviour seemed to generally be kinder. Teaching in particular was much more focused on support and reasoned discussion. Yes, people were still often less-than-nice to each other, but hunger games pedagogies and gladiatorial argument seemed to have largely disappeared.

Yet, and yet, here it all is. Still. But now on social media. Not in the relative privacy of a tutorial or seminar room, but full frontal in public. Just not face to face. A senior academic bloke flinging insults into the ether without actually witnessing what happens as a result to the person under attack, to others who are watching or trying to support, or to social media practices/cultures. And then the hangers on join in. Triggered by their ‘guru’ they wade in. More mass nastiness – distanced, much of it anonymous, some of it just smart-assery, a lot of it rage and bile.

This is not the first such incident*. This kind of joustery-to-the-death makes social media a very unhospitable place. Somewhere where narcissism prevails, where tribalisms are constructed and reinforced and a pernicious anti-ethics prevails.

And this kind of social-mediatised behaviour is not something that can be fixed with calls for academic niceness and kindness. We need to name this badness for what it is. It’s not clever. It’s not a sign of intellect or learning. It’s just harassment. Plain old bullying. And sometimes just criminal hate.

Civility in academic discussion – what Martha Nussbaum might call a practice of moral emotions which recognises and values other persons and perspectives – seems sadly at risk on social media. Bravo Mary Beard for continuing to demonstrate what it means.

As many have already noted in print and on social media, red in twitter tooth and claw is academic conduct unbecoming and unwarranted. Encouraging others in attack behaviour in the name of some kind of robustness or anti-‘political correctness’ is equally, perhaps even more noxious.

So what can be done, other than to back off, or to persist graciously and courteously, as Mary Beard has done. Well, at a time when we academics are required to engage more with publics and public debate, we need our institutions to openly condemn violent and combatorial social media practice.Alison Phipps made this point quite some time ago, calling for universities to consider the impact agenda and what it asked of us. She noted that the ways in which those who spoke about questions of equity, gender and race in particular were liable to be vilified and threatened. Phipps called for some kind of institutional action.

Nothing much seems to have happened in the three years since her call for intervention. Institutional ‘duty of care’ seems still to be a notable absence rather than an active presence in social media turmoils . Yet the impact and public engagement conversation continues unabated  – and as if none of this nastiness happens.

Those of us who have no stomach for social media verbal violence need to know that we have institutional backing against macho belligerence – torrents of crude abuse cannot masquerade as intelligent debate. Less Coliseum, more Socratic dialogue please. Universities – we need your explicit and open support if we are to speak about our work with publics who aren’t always receptive, prepared to listen or to behave decently.

 

 

*A new edited book contains systematic analysis of these kinds of incidents. Watch out for BAROUTSIS, A, RIDDLE, S and THOMSON, P Eds Education Research and the Media: Challenges and Possibilities Routledge – coming in late 2018.

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#holidayreading – the forgotten tribe, scientists as writers

Over my “holiday”, actually not holiday I’m working, I’m catching up on research about writing. In fact, I’ve just been reading about the writing practices of scientists.

Lisa Emerson interviewed 106 scientists –  distributed across four countries and multiple science disciplines (but excluding pure mathematics). She wanted to address/redress the common myth that scientists write badly and don’t care that they do. They allegedly churn out tedious experimental reports that nobody can or wants to read. 

I’m with Emerson on debunking this, if it is something that people believe – some great academic writing comes from scientists. Witness this year’s Royal Society short list for science writing for instance. The list includes Ed Yong’s I contain multitudes and Cordelia Fine’s Testosterone Rex, both of which are highly pleasurable reads, as well as being worthily informative.

The starting point for Emerson’s research was that postgraduate scientists generally learn academic writing differently from those of us in the arts and social sciences. Working in lab teams, postgraduate scientists tend to co-write with a supervisor or with peers. This apprenticeship model differs from the ways in which say, a literature scholar works alone in a library on text, shows their writing to a supervisor and gets feedback. There is no co-writing. Because of this difference, the writing habits of scientists needed a specific research project, Emerson asserts.

coverBefore reading the book, I’d have assumed that some scientists do see writing as very important and also see themselves as writers, whereas others don’t –  much like the rest of the academy then. And indeed, that is actually what Emerson found. Emerson divides her scientist interviewees into two broad groups – those who are routine writers, they don’t like writing much, but they see it as part of the work and they do it – and adaptive writers – they like writing, even if it is hard work sometimes. Adaptive writers spend time working on their writing craft, and experiment with a range of genres and styles of writing. Even as senior scholars, they still write a lot and don’t delegate writing to junior members of their team.

Emerson’s interest is in how her group got to be either routine or adaptive. She is not judgmental about a career choice to engage widely with the public or not  – both are equally valid and both are needed she says. Rather, her concern is with the patchiness of learning about science writing and how that might be understood and changed.

So, here’s a bit of a potted summary of some of her key points.

  • Adaptive writers tended to have positive experiences of writing during childhood and school – and actually most of her adaptive and routine writers had taken language rich subjects at school so this wasn’t about subject choice. But, Emerson notes, schools could do a much better job of teaching scientific writing.
  • Scientific writing was largely learnt through co-authorship, supervision, reading and imitation with most scientists,  routine and adaptive, having negative experiences of working with an advisor. Positive experiences were where supervisors talked through changes – not just rewriting or asking for changes without explanation which was the more common experience. (Emerson’s interviewees did produce some teethgritting examples of worst practice; these are always instructive.)
  • Very few of the entire group had any assistance with guided reading designed to help them learn the rhetorical conventions of their discipline – they learnt this through a kind of immersion in the discipline rather than anything explicit.
  • Support for writing post PhD, including participation in writing groups, was important for all of the scientist interviewees. Mentorship was key, with adaptive writers reporting higher levels of support than routine writers. Two of the group had experienced specific writing programmes during their undergraduate years and reported the significance of this experience. 

Emerson argues that the lack of undergraduate attention to scientific writing, and patchy experiences of the postgraduate apprenticeship mode, clearly point to the kinds of  interventions that might improve scientific community engagement with writing. As she see it, “the challenge for the scientific community is to begin to model and articulate the attitudes and beliefs towards writing that they wish to see in their graduate students”. Emerson sees an answer not in courses or modelling per se, but rather in ongoing and deliberate conversations about writing, conversations that go on throughout an academic science career.

I reckon that this is the kind of book that ought to be part of a resource kit for supervisor development. I can imagine that a discussion of extracts from this book could kick off a pretty interesting intervention to bring writing into greater focus in teaching/learning/supervision. The book isobviously of interest to those in science but it does also have some pointers for those in other disciplinary areas too, particularly as lab-based models of research spread more widely across the academy.

And – yay – Emerson’s book is open access online. 

Posted in academic writing, Lisa Emerson, science writing | Tagged , , | 2 Comments