Researcher as Teenager: Parsing Danah Boyd’s It’s Complicated

I have a distinct tendency to see everything through the lens of what it means for research communities. I have just finally read Danah Boyd’s It’s Complicated a book that focuses on how and why U.S. teenagers interact with and through social media. The book is well worth reading for the study itself, but I would argue it is more worth reading for the way it challenges many of the assumptions we make about how social interactions online and how they are mediated by technology.

The main thrust of Boyd’s argument is that the teenagers she studied are engaged in a process of figuring out what their place is amongst various publics and communities. Alongside this she diagnoses a long standing trend of reducing the availability of the unstructured social interactions through which teens explore and find their place.

A consistent theme is that teens go online not to escape the real world, or because of some attraction to the technology but because it is the place where they can interact with their communities, test boundaries and act out in spaces where they feel in control of the process. She makes the point that through these interactions teens are learning how to be public and also how to be in public.

So the interactions and the needs they surface are not new, but the fact that they occur in online spaces where those interactions are more persistent, visible, spreadable and searchable changes the way in which adults view and interact with them. The activities going on are the same as in the past: negotiating social status, sharing resources, seeking to understand what sharing grants status, pushing the boundaries, claiming precedence and seeking control of their situation.

Boyd is talking about U.S. teenagers but I was consistently struck by the parallels with the research community and its online and offline behavior. The wide prevalence of imposter syndrome amongst researchers is becoming better known – showing how strongly the navigation and understanding of your place in the research community effects even senior researchers. Prestige in the research community arises from two places, existing connections (where you came from, who you know) and the sharing of resources (primarily research papers). Negotiating status, whether offline or on, remains at the core of researcher behavior throughout careers. In a very real sense we never grow up.

People generally believe that social media tools are designed to connect people in new ways. In practice, Boyd points out, mainstream tools effectively strengthen existing connections. My view has been that “Facebooks for Science” fail because researchers have no desire to be social as researchers in the same way the do as people – but that they socialize through research objects. What Boyd’s book leads me to wonder is whether in fact the issue is more that the existing tools do little to help researchers negotiate the “networked publics” of research.

Teens are learning and navigating forms of power, prestige and control that are highly visible. The often do this through sharing objects that are easily intepretable, text and images (although see the chapter on privacy for how this can be manipulated). The research community buries those issues because we would like to think we are a transparent meritocracy.

Where systems have attempted to surface prestige or reputation in a research context through point systems they have never really succeeded. Partly this is because those points are not fungible – they don’t apply in the “real” world (StackExchange wins in part precisely because those points did cross over rapidly into real world prestige). Is it perhaps precisely our pretence that this sense-making and assignment of power and prestige is supposed to be hidden that makes it difficult to build social technologies for research that actually work?

An Aside: I got a PDF copy of the book from Danah Boyd’s website because a) I don’t need a paper copy and b) I didn’t want to buy the ebook from Amazon. What I’d really like to do is buy a copy from an independent bookstore and have it sent somewhere where it will be read, a public or school library perhaps. Is there an easy way to do that?

A little bit of federated Open Notebook Science

Girl Reading a Letter at an Open Window
Image via Wikipedia

Jean-Claude Bradley is the master when it comes to organising collaborations around diverse sets of online tools. The UsefulChem and Open Notebook Science Challenge projects both revolved around the use of wikis, blogs, GoogleDocs, video, ChemSpider and whatever tools are appropriate for the job at hand. This is something that has grown up over time but is at least partially formally organised. At some level the tools that get used are the ones Jean-Claude decides will be used and it is in part his uncompromising attitude to how the project works (if you want to be involved you interact on the project’s terms) that makes this work effectively.

At the other end of the spectrum is the small scale, perhaps random collaboration that springs up online, generates some data and continues (or not) towards something a little more organised. By definition such “projectlets” will be distributed across multiple services, perhaps uncoordinated, and certainly opportunistic. Just such a project has popped up over the past week or so and I wanted to document it here.

I have for some time been very interested in the potential of visualising my online lab notebook as a graph. The way I organise the notebook is such that it, at least in a sense, automatically generates linked data and for me this is an important part of its potential power as an approach. I often use a very old graph visualisation in talks I give out the notebook as a way of trying to indicate the potential which I wrote about previously, but we’ve not really taken it any further than that.

A week or so ago, Tony Hirst (@psychemedia) left a comment on a blog post which sparked a conversation about feeds and their use for generating useful information. I pointed Tony at the feeds from my lab notebook but didn’t take it any further than that. Following this he posted a series of graph visualisations of the connections between people tweeting at a set of conferences and then the penny dropped for me…sparking this conversation on twitter.

@psychemedia You asked about data to visualise. I should have thought about our lab notebook internal links! What formats are useful? [link]

@CameronNeylon if the links are easily scrapeable, it’s easy enough to plot the graph eg http://blog.ouseful.info/2010/08/30/the-structure-of-ouseful-info/ [link]

@psychemedia Wouldn’t be too hard to scrape (http://biolab.isis.rl.ac.uk/camerons_labblog) but could possibly get as rdf or xml if it helps? [link]

@CameronNeylon structured format would be helpful… [link]

At this point the only part of the whole process that isn’t publicly available takes place as I send an email to find out how to get an XML download of my blog and then report back  via Twitter.

@psychemedia Ok. XML dump at http://biolab.isis.rl.ac.uk/camerons_labblog/index.xml but I will try to hack some Python together to pull the right links out [link]

Tony suggests I pull out the date and I respond that I will try to get the relevant information into some sort of JSON format, and I’ll try to do that over the weekend. Friday afternoons being what they are and Python being what is I actually manage to do this much quicker than I expect and so I tweet that I’ve made the formatted data, raw data, and script publicly available via DropBox. Of course this is only possible because Tony tweeted the link above to his own blog describing how to pull out and format data for Gephi and it was easy for me to adapt his code to my own needs, an open source win if there ever was one.

Despite the fact that Tony took the time out to put the kettle on and have dinner and I went to a rehearsal by the time I went to bed on Friday night Tony had improved the script and made it available (with revisions) via a Gist, identified some problems with the data, and posted an initial visualisation. On Saturday morning I transfer Tony’s alterations into my own code, set up a local Git repository, push to a new Github repository, run the script over the XML dump as is (results pushed to Github). I then “fix” the raw data by manually removing the result of a SQL insertion attack – note that because I commit and push to the remote repository I get data versioning for free – this “fixing” is transparent and recorded. Then I re-run the script, pushing again to Github. I’ve just now updated the script and committed once more following further suggestions from Tony.

So over a couple of days we used Twitter for communication, DropBox, GitHub, Gists, and Flickr for sharing data and code, and the whole process was carried out publicly. I wouldn’t have even thought to ask Tony about this if he hadn’t  been publicly posting his visualisations (indeed I remember but can’t find an ironic tweet from Tony a few weeks back about it would be clearly much better to publish in a journal in 18 months time when no-one could even remember what the conference he was analysing was about…).

So another win for open approaches. Again, something small, something relatively simple, but something that came together because people were easily connected in a public space and were routinely sharing research outputs, something that by default spread into the way we conducted the project. It never occurred to me at the time, I was just reaching for the easiest tool at each stage, but at every stage every aspect of this was carried out in the open. It was just the easiest and most effective way to do it.

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The personal and the institutional

Twittering and microblogging not permitted
Image by cameronneylon via Flickr

A number of things recently have lead me to reflect on the nature of interactions between social media, research organisations and the wider community. There has been an awful lot written about the effective use of social media by organisations, the risks involved in trusting staff and members of an organisation to engage productively and positively with a wider audience. Above all there seems a real focus on the potential for people to embarrass the organisation. Relatively little focus is applied to the ability of the organisation to embarrass its staff but that is perhaps a subject for another post.

In the area of academic research this takes on a whole new hue due to the presence of a strong principle and community expectation of free speech, the principle of “academic freedom”. No-one really knows what academic freedom is. It’s one of those things that people can’t define but will be very clear about when it has been taken away. In general terms it is the expectation that a tenured academic has earnt the right to be able to speak their opinion, regardless of how controversial. We can accept there are some bounds on this, of ethics, taste, and legality – racism would generally be regarded as unacceptable – while noting that the boundary between what is socially unacceptable and what is a validly held and supported academic opinion is both elastic and almost impossible to define. Try expressing the opinion, for example, that their might be a biological basis to the difference between men and women on average scores on a specific maths test. These grey areas, looking at how the academy ( or academies) censor themselves are interesting but aren’t directly relevant to this post. Here I am more interested in how institutions censor their staff.

Organisations always seek to control the messages they release to the wider community. The first priority of any organisation or institution is its own survival. This is not necessarily a bad thing – presumably the institution exists because it is  (or at least was) the most effective way of delivering a specific mission. If it ceases to exist, that mission can’t be delivered. Controlling the message is a means of controlling others reactions and hence the future. Research institutions have always struggled with this – the corporate centre sending once message of clear vision, high standards, continuous positive development, while the academics privately mutter in the privacy of their own coffee room about creeping beauracracy, lack of resources, and falling standards.

There is fault on both sides here. Research administration and support only very rarely puts the needs and resources of academics at its centre. Time and time again the layers of beauracracy mean that what may or may not have been a good idea gets buried in a new set of unconnected paperwork, that more administration is required taking resources away from frontline activities, and that target setting results in target meeting but at the cost of what was important in the first place. There is usually a fundamental lack of understanding of what researchers do and what motivates them.

On the other side academics are arrogant and self absorbed, rarely interested in contributing to the solution of larger problems. They fail to understand, or take any interest in the corporate obligations of the organisations that support them and will only rarely cooperate and compromise to find solutions to problems. Worse than this, academics build social and reward structures that encourage this kind of behaviour, promoting individual achievement rather than that of teams, penalising people for accepting compromises, and rarely rewarding the key positive contribution of effective communication and problem solving between the academic side and administration.

What the first decade of the social web has taught us is that organisations that effectively harness the goodwill of their staff or members using social media tools do well. Organisations that effectively use Twitter or Facebook enable and encourage their staff to take the shared organisational values out to the wider public. Enable your staff to take responsibility and respond rapidly to issues, make it easy to identify the right person to engage with a specific issue, and admit (and fix) mistakes early and often, is the advice you can get from any social media consultant. Bring the right expert attention to bear on a problem and solve it collaboratively, whether its internal or with a customer. This is simply another variation on Michael Nielsen’s writing on markets in expert attention – the organisations that build effective internal markets and apply the added value to improving their offering will win.

This approach is antithetical to traditional command and control management structures. It implies a fluidity and a lack of direct control over people’s time. It is also requires that there be slack in the system, something that doesn’t sit well with efficiency drives. In its extreme form it removes the need for the organisation to formally exist, allowing a fluid interaction of free agents to interact in a market for their time. What it does do though is map very well onto a rather traditional view of how the academy is “managed”. Academics provide a limited resource, their time, and apply it to a large extent in a way determined by what they think is important. Management structures are in practice fairly flat (and used to be much more so) and interactions are driven more by interests and personal whim than by widely accepted corporate objectives. Research organisations, and perhaps by extension those commercial interests that interact most directly with them, should be ideally suited to harness the power of the social web to first solve their internal problems and secondly interact more effectively with their customers and stakeholders.

Why doesn’t this happen? A variety of reasons, some of them the usual suspects, a lack of adoption of new tools by academics, appalling IT procurement procedures and poor standards of software development, and a simple lack of time to develop new approaches, and a real lack of appreciation of the value that diversity of contributions can bring to a successful department and organisation. The biggest one though I suspect is a lack of good will between administrations and academics. Academics will not adopt any tools en masse across a department, let alone an organisation because they are naturally suspicious of the agenda and competence of those choosing the tools. And the diversity of tools they choose on their own means that none have critical mass within the organisation – few academic institutions had a useful global calendar system until very recently. Administration don’t trust the herd of cats that make up their academic staff to engage productively with the problems they have and see the need to have a technical solution that has critical mass of users, and therefore involves a central decision.

The problems of both diversity and lack of critical mass are a solid indication that the social web has some way to mature – these conversations should occur effectively across different tools and frameworks – and the uptake at research institutions should (although it may seem paradoxical) be expected to much slower than in more top down, managed organisation, or at least organisations with a shared focus. But it strikes me that the institutions that get this right, and they won’t be the traditional top institutions, will very rapidly accrue a serious advantage, both in terms of freeing up staff time to focus on core activities and releasing real monetary resource to support those activities. If the social side works, then the resource will also go to the right place. Watch for academic institutions trying to bring in strong social media experience into senior management. It will be a very interesting story to follow.

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What should social software for science look like?

Nat Torkington, picking up on my post over the weekend about the CRU emails takes a slant which has helped me figure out how to write this post which I was struggling with. He says:

[from my post...my concern is that in a kneejerk response to suddenly make things available no-one will think to put in place the social and technical infrastructure that we need to support positive engagement, and to protect active researchers, both professional and amateur from time-wasters.] Sounds like an open science call for social software, though I’m not convinced it’s that easy. Humans can’t distinguish revolutionaries from terrorists, it’s unclear why we think computers should be able to.

As I responded over at Radar, yes I am absolutely calling for social software for scientists, but I didn’t mean to say that we could expect it to help us find the visionaries amongst the simply wrong. But this raises a very helpful question. What is it that we would hope Social Software for Science would do? And is that realistic?

Over the past twelve months I seem to have got something of a reputation for being a grumpy old man about these things, because I am deeply sceptical of most of the offerings out there. Partly because most of these services don’t actually know what it is they are trying to do, or how it maps on to the success stories of the social web. So prompted by Nat I would like to propose a list of what effective Social Software for Science (SS4S) will do and what it can’t.

  1.  SS4S will promote engagement with online scientific objects and through this encourage and provide paths to those with enthusiasm but insufficient expertise to gain sufficient expertise to contribute effectively (see e.g. Galaxy Zoo). This includes but is certainly not limited to collaborations between professional scientists. These are merely a special case of the general.
  2. SS4S will measure and reward positive contributions, including constructive criticism and disagreement (Stack overflow vs YouTube comments). Ideally such measures will value quality of contribution rather than opinion, allowing disagreement to be both supported when required and resolved when appropriate.
  3. SS4S will provide single click through access to available online scientific objects and make it easy to bring references to those objects into the user’s personal space or stream (see e.g. Friendfeed “Like” button)
  4. SS4S should provide zero effort upload paths to make scientific objects available online while simultaneously assuring users that this upload and the objects are always under their control. This will mean in many cases that what is being pushed to the SS4S system is a reference not the object itself, but will sometimes be the object to provide ease of use. The distinction will ideally be invisible to the user in practice barring some initial setup (see e.g. use of Posterous as a marshalling yard).
  5. SS4S will make it easy for users to connect with other users and build networks based on a shared interest in specific research objects (Friendfeed again).
  6. SS4S will help the user exploit that network to collaboratively filter objects of interest to them and of importance to their work. These objects might be results, datasets, ideas, or people.
  7. SS4S will integrate with the user’s existing tools and workflow and enable them to gradually adopt more effective or efficient tools without requiring any severe breaks (see Mendeley/Citeulike/Zotero/Papers and DropBox)
  8. SS4S will work reliably and stably with high performance and low latency.
  9. SS4S will come to where the researcher is working both with respect to new software and also unusual locations and situations requiring mobile, location sensitive, and overlay technologies (Layar, Greasemonkey, voice/gesture recognition – the latter largely prompted by a conversation I had with Peter Murray-Rust some months ago).
  10. SS4S will be trusted and reliable with a strong community belief in its long term stability. No single organization holds or probably even can hold this trust so solutions will almost certainly need to be federated, open source, and supported by an active development community.

What SS4S won’t do is recognize geniuses when they are out in the wilderness amongst a population of the just plain wrong. It won’t solve the cost problems of scientific publication and it won’t turn researchers into agreeable, supportive, and collaborative human beings. Some things are beyond even the power of Web 2.0

I was originally intending to write this post from a largely negative perspective, ranting as I have in the past about how current services won’t work. I think now there is a much more positive approach. Lets go out there and look at what has been done, what is being done, and how well it is working in this space. I’ve set up a project on my new wiki (don’t look too closely, I haven’t finished the decorating) and if you are interested in helping out with a survey of what’s out there I would appreciate the help. You should be able to log in with an OpenID as long as you provide an email address. Check out this Friendfeed thread for some context.

My belief is that we are near to position where we could build a useful requirements document for such a beast with references to what has worked and what hasn’t. We may not have the resources to build it and maybe the NIH projects currently funded will head in that direction. But what is valuable is to pull the knowledge together to figure out the most effective path forward.