Using the technique described in Estimated Follower Accession Charts for Twitter, we can estimate a follower acquisition growth curve for the @imascientist Twitter account:

I’ve already noted how we may be able to use “spikes” in follower acquisition rates to identify news events that involved the owner of a particular Twitter account and caused a surge in follower numbers as a result (What Happened Then? Using Approximated Twitter Follower Accession to Identify Political Events).

Thinking back to the context of evaluating the impact of events that include social media as part of the overall campaign, it struck me that whilst running a particular event may not lead to a huge surge in follower numbers on the day of the event or in the immediate aftermath, the followers who do sign up over that period might have signed up as a result of the event. And now we have the first inklings of a post hoc analysis tool that lets us try to identify these people, and perhaps look to see if their profiles are different to profiles of followers who signed up at different times (maybe reflecting the audience interest profile of folk who attended a particular event, or reflecting sign ups from a particular geographical area?)

In other words, through generating the follower acquisition curve, can we use it to filter down to folk who started following around a particular time in order to then see whether there is a possibility that they started following as a result of a particular event, and if so can count as some sort of “conversion”? (I appreciate that there are a lot of caveats in there!;-)

A similar approach may also be relevant in the context of analysing link building around historical online community events, such as MOOCs… If we know somebody took a particular MOOC at a particular time, might we be able to construct their follower acquisition curve and then analyse it around the time of the MOOC, looking to see if the connections built over that period are different to the users other followers, and as such may represent links developed as a result of taking the MOOC? Analysing the timelines of the respective parties may further reveal conversational dynamics between those parties, and as such allow is to see whether a fruitful social learning relationship developed out of contact made in the MOOC?

To pull the post together, I drew on a Guardian Technology editor Charles Arthur’s retweets over the course of the evening, which provided an interesting technological slant on the news as it was breaking/developing.

The event also prompted a series of posts on the role of Twitter in reporting breaking news. Twitter and news: The canary down the mine compares the dynamics of Twitter in a breaking news situation in the context of the “news funnel”:

One of the theories you learn when you train to be a journalist is the “funnel” of news. Imagine a funnel. It’s getting all the information about a certain news story poured into it – from the top. Wild rumours and hard facts. Witness accounts alongside back-of-a-cigarette packet theories,

The funnel is the journalist. And the funnel’s job is to take all the information, from the crazy and the correct, and pour it, with a measure of control, into the story. Take out the impurities, crush up the lumps, and make the resulting article a distillation of the thousands of snippets, with no errors.

It seems to me, very often these days, that Twitter is the funnel turned upside down.

All of those disparate ingredients are poured into the nozzle. The narrow part. And they are not filtered, not regulated, not tested. But they come out of the wide part of the funnel. They are spread across a large area. Indiscriminately.

And that’s how Twitter appeared to me on the night of the Boston marathon bombs.

Another review of the event used a form of topic analysis to explore the semantic textual content different waves of Twitter activity using a Twitter accession count timebase – The evolution of discussion around the Boston Marathon events.

PS in the context of Twitter and the news, I note that Guardian Datablog editor Simon Rogers is going to work for Twitter, to be replaced at the Guardian by James Ball… I also spotted a few days ago that ex-of the Guardain data intereactive designer Alastair Dant had also moved to the New York Times.

Arguably primed by the open courseware and open learning initiatives that started a decade or so ago (precursor: OSTP), several notable MOOC platforms (Coursera, Udacity) provide a one stop supermarket for 20-100 hour large cohort, online “course experiences” offered by traditional universities. Using a blend of readings and video lectures, courses provide pacing through a predetermined syllabus on a particular topic, with social tools to allow students to discuss elements of the course with each other. To provide the feedback on progress, computer marking systems and scaleable “peer assessment” provide a means of letting a student know how well they are doing on the course.

At least, I think that’s how it works. I don’t really know. Though I’ve signed up for several MOOCs, I’ve never actually started any of them, or tried to work through them.

Maybe that’s because I tend to learn from resources discovered on the web in response to questions I’ve formulated myself (questions which often derive from reading other resources and either being confused by them or not being able to make sense of them!). But just see how far a search far a web search for visualisation site:coursera.org gets you. As I believe Patrick McAndrew suggested. (Hmmm… I appear to have hit my article limit… Here’s Patrick’s OER13 keynote which I think led to the article.)

I’m not sure who, within the universities that have signed up to delivering courses on the various MOOC platforms, is deciding which courses to run with, but I suspect the marketing department has a hand in it.

Marketing departments also used to run university web presences, too, didn’t they?

Way back when, when I was still at school, I used to watch tech documentaries – I remember seeing a window based GUI for the first time on an old episode of Horizon – and OU programmes, amongst other things… If you’re over a certain age, you’ll remember them:

Things have moved on since then, of course. OU/BBC programmes now are of a far more mainstream flavour (for example, clips from recent OU/BBC co-pros currently on iPlayer). Add to that, a wide variety of online videos, such as the 60 second animation series (such as 60 Second Adventures in Thought, or 60 Second Adventures in Astronomy, or even 60 Second Adventures in Economics), or clips from OU produced videos sent to students as part of their course materials (this sort of thing, for example: A Cyborg Future?, The Buildings of Ancient Rome or Environmental Policy in an International Context.)

As “tasters”, the OU course programmes that appeared in the dead parts of the schedule on BBC2 introduced me to a particular form of discourse, somewhere between a lecture and a tutorial, at particular level of understanding (higher education academic). The programmes were created as embedded parts of a distance education course, complementing readings and exercises, and though I find it hard to remember back, I think that came across. That the programmes were a glimpse into a wider, and deeper, exploration of a particular topic that the course of which they were a part provided. I don’t really recall.

So whilst the OU course programmes were part of a course, they were not the whole of it. They were windows into a course, rather than a microcosm of one. I get the impression that the MOOCs are intended in some way to be “complete”, albeit short, courses, that are maybe intended to act as tasters of more comprehensive, formal offerings. I don’t really know.

My introduction to the OU, then, aged ten, or thereabouts, so 35 years or so ago, was through these glimpses into courses that other people were studying. They were opportunities for me to walk into a lecture to see what it was like. The programmes were not intended for me, but I could partake of them. That is more of a “passively discoverable OER” model than a MOOC. Maybe. I don’t really know.

I wonder now, if now was then, how I would have come to discover the world of “academic” communications. Through Google, presumably. Through the web. Through the marketing department? Or through the academics, (but discovered how?).

I guess we could argue that MOOCs represent, in part, higher education marketing departments waking up to the fact that the web exists, that it is a contentful medium, in part at least, and that the universities have content that may attract eyeballs. Maybe. I don’t really know.

If the marketing departments are leading the MOOC campaigns, I wonder what sort of return they expect? Raising “brand awareness”? Being seen to be there/having a presence on a platform because other universities have? Generating leads and building up mailing lists? Online courses as “promotional items” (whatever that means)? Edutainment offerings?! I don’t really know.

Going back to the OU programmes on BBC2, the primary audience then were presumably students on a course, because the programmes were part of the course. Partially open courses. Courses being run for real that also had an open window open on to them, so that other people could see what sorts of thing were covered by those courses, and maybe learn a little along the way.

This is more in keeping with the model of online course delivery being pursued by Jim Groom’s ds106 or Martin Weller’s H817 module on “Open Education” (I think; I don’t really know). I think. I don’t really know.

Other models are possible, of course. The “cMOOC” – connectionist/connectivist MOOC – idea explores a different pedagogy. The xMOOC offerings of Coursera and Udacity wrap not opencourseware in a delivery platform and run scheduled cohorts. The original OU OpenLearn offering had the platform (Moodle), had the open content, but didn’t have the community that comes from marshalling learners into a scheduled offering. Or the hype. Or at least, not the right sort of hype (the hype that follows VC investment, where VC does not refer to Vice Chancellor). The cMOOC idea tries to be open as to curriculum, in part – a more fluid learning environment where loose co-ordination amongst participants encourages an element of personal research into a topic and sharing of the lessons learned. A pedagogy that seeks to foster independent learning in the sense of being able to independently research a topic, rather than independently pay to follow a prescribed path through a set of learning materials. In the xMOOC, a prescribed path that propagates a myth of there being one true way to learn a subject. One true path.

My own open course experiment was an uncourse. Tasked with writing a course on a subject about which I knew nothing, I sought to capture my own learning path through it, using that trail to inform the design of a supposedly more polished offering. The traces are still there, still open – Digital Worlds – Interactive Media and Game Design. The pages still get hit, the resources still get downloaded. I could – should – do a little more to make evident the pathways through the content.

Whilst the reverse chronological form of a blog made sense as I was discovering a trail through the subject area – new content was revealed to any others following the uncourse in a sensible order – looking back at the material now the journeys through each topic area start at the end, presenting anyone wishing to follow the path I took with an apparent problem. Though not really… If you select a Category on the Digital Worlds blog, and add ?order=asc – as for example http://digitalworlds.wordpress.com/category/animation/?order=asc, the posts will be presented in chronological order. I wonder if there is a switch – or a plugin – that can make chronological views of posts within a particular tag or category on WordPress automatically display in a chronological order? I don’t really know. This would provide one way of transforming a platform configured as a “live presentation” site into one that worked better as a legacy site. It’s not hard to imagine a Janus theme that would provide these two faces of a site, one in reverse chronological order for live delivery, the other in chronological order for folk wishing to follow the steps taken by a previous journeyman in the same order as they were originally taken.

I still don’t know what forces are at play that may result in MOOC-hype and whatever shakes out as a result transforming, if at all, higher education as we know it and as developing countries may yet come to know it. I really don’t know.

And I still don’t have a good feeling for how we can make most effective use of the web to support a knowledge driven society; how best we can make use of online content resources and social communication tools to help people to develop their own personal, and deeper, understanding about whatever topic, to help them make sense of whatever they need to make sense of; how best schools and universities can draw on the web to help people develop lifelong learning skills; what it means to use the web in furtherance of lifelong learning.

I really, really, don’t know.

and had a beer:

Very – apt.

I’m normally a bitter drinker (read that how you will…) but when in, erm, Rome, I figured I should do as the, erm, doughnuts do, and have what presumably translates back here as “a continental beer”:

Good trip though:-)

Here’s what we get in the zipped datafile:

The school level dataset had the following column headings:

Year, country_code, country, GOR, GOR_code, LA, new_LA_code, LA_Name, URN, Estab, LAEstab, School_name, School_type, Academy_Flag, Academy_open_date, enrol_sum, SessionsPossible_sum, OverallAbsence_sum, AuthorisedAbsence_sum, UnauthorisedAbsence_sum, overall_absence_percent, auth_absence_percent, unauth_absence_percent, PA_15_sum, possible_sessions_pa_15_sum, overall_abs_pa_15_sum, auth_abs_pa_15_sum, unauth_abs_pa_15_sum, overall_absence_percent_PA_15, auth_absence_percent_PA_15, unauth_absence_percent_PA_15, sess_auth_illness, sess_auth_appointments, sess_auth_religious, sess_auth_study, sess_auth_traveller, sess_auth_holiday, sess_auth_ext_holiday, sess_auth_excluded, sess_auth_other, sess_auth_totalreasons, sess_auth_unclass, sess_unauth_holiday, sess_unauth_late, sess_unauth_other, sess_unauth_noyet, sess_unauth_totalreasons, sess_unauth_unclass, sess_overall_totalreasons

We can guess at what some of these refer to, but what, for example, do the “PA 15″ columns refer to? In this case, what we really should do is look up the actual definitions, which are described in the metadata description document; a document that just happens to be a Microsoft Word 2007 formatted document…

…a document that doesn’t play nicely either with the copy of Word I have on my Mac:

…or the converter that the Google docs uploader uses:

In cases such as this, particularly where there are mathematical equations that often have very specific layout requirements, it can be “safer” to use a document format such as PDF that more reliably captures the appearance of the original page. (If we were really keen on reproducibility, we might also suggest that the equations were made available in an executable form, such as programme code or even as a spreadsheet (I’m not sure “Microsoft equations” are executable?).)

I gave myself a couple of hours to have a quick look through some of the data, but as it is I’ve spent an hour or so looking for ways of reading the metadata description document along with writing up my frustration around not being able to do so… Which is time spent not making sense of the data, or, indeed, its metadata…

PS in passing, I note the publication of the parliamentary Public Accounts Committee 37th report, Whole of Government Accounts 2010-11 again picks up on the way in which government data releases often fall short in terms of their usability (for example, this week MPs call for greater use of Whole of Government Accounts; see also last August Government must do better on transparency, say MPs).

PPS Here’s the solution I used in the end – Skydrive, Microsoft’s online storage/doc viewing play:

As it turns out, the equations could easily have been written using simple text strings…

PPPS as to the “15″ columns, the metadata files describes them along the following lines:

PA_15_sum Number of enrolments classed as persistent absentees (threshold of 15 per cent)

possible_session_pa_15_sum Sessions possible for persistent absentees (threshold of 15 per cent)

Which means what exactly?!

A tweet confirmed that this wasn’t intentional, so what had happened? I gather the workflow used to generate the post was to write it in Google docs, and then copy and paste the rich/HTML text into a rich text editor in Drupal, although I couldn’t recreate this effect (and nor could FullFact). However, suitably suspicious, I started having a play, writing a simple test document in Google docs:

The Google doc automatically links the test URL I added to the document. (This is often referred to as “linkification” – if a piece of text is recognised as something that looks like a URL or web link, it gets rewritten as a clickable link. Typically, you might assume that the link you’ll now be clicking on is the link that was recognised. This may be a bad assumption to make…) If you hover over the URL as written in the document, you get a tooltip that suggests the link is to the same URL. However, if you hover over the tooltip listed URL, (or click on it) you can see from the indicator in the bottom left hand corner of the browser what the actual URL you’re clicking on is. Like this:

In this case, the link you’ll actually click on is referral to the original link via a Google URL. This one, in fact:

http://www.google.com/url?q=http%3A%2F%2Fblog.ouseful.info&sa=D&sntz=1&usg=AFQjCNHgu25L-v9rkkMqZSX54E8kP_XR-A

What this means is that if I click on the link, Google tracks the fact that the link was clicked on. From the value of the usg variable (in this case, AFQjCNHgu25L-v9rkkMqZSX54E8kP_XR-A) it presumably also knows the source document containing the link and whatever follows from that.

Hmmm… If I publish the document, the Google rewrite appears to be removed:

There are also several export options associated with the document:

So what links get exported?

Here’s the Word export:

That seems okay – no tracking. How about odt?

That looks okay too. RTF and and HTML export also seem to publish the “clean” link.

What about PDF?

Hmm… so tracking is included here. So if you write a doc in Google docs that contains links that are autolinked, then you export that doc as PDF and share it with other folk, Google will know when folk click on that link from a copy of that PDF document and (presumably) the originally authored Google docs document (and all that that entails…)

How about if we email a doc as a PDF attachment to someone from within Google docs:

So that seems okay (untracked).

What’s the story then? FullFact claimed they cut and paste rich HTML from Google docs into a rich text editor and the Google redirection attack was inserted into the link. I couldn’t recreate that, and nor could the FullFact folk, so either there are some Google “experiments” going on, or the workflow was misremembered.

In my own experiments, I got a Google redirection from clicking links within my original document, and from the exported PDF, but not from any other formats?

So what do we learn? I guess this at least: be aware that when Google linkifies links for you, it may be redirecting clicks on those links through Google tracking servers. And that these tracked links may be propagated to exported and/or otherwise shared versions of the document.

PS see also Google/Feedburner Link Pollution or More Link Pollution – This Time from WordPress.com for more of the same, and Personal Declarations on Your Behalf – Why Visiting One Website Might Tell Another You Were There for a quick overview of what might happen when you actually land on a page…

Link rewriters are, of course, to be find in lots of other places too…

Twitter, for example, actually wraps all shared links in it’s t.co wrapper:

Delicious (which I’ve stopped using – I moved over to Pinboard) also uses it’s own proxy for clicked on stored bookmarks…

If you have any other examples, particularly of link rewriting/annotation/pollution where you wouldn’t expect it, please let me know via the comments…

A recent THES feature article – Are universities as open as they should be? – casts an eye over FOI requests in a university context, as well as transparency relating to university decition-making, pay (and bonuses) and the make-up of the student body:

With universities increasingly relying on funding from private sources, some believe that the burden placed on universities by the [Freedom of Information] Act is unfair and unsustainable. “As funding of the sector changes, and a greater proportion of university income comes from non-governmental sources, it is even more arguable whether universities are truly public authorities and whether the Act should therefore apply to them,” the University of Essex told MPs on the Commons Justice Committee when they audited the Act last year.
…
Universities can be unforthcoming for different reasons. In response to recent FoI requests submitted by Times Higher Education, a number of universities have refused to divulge information because they say it could harm their commercial interests – an exemption permitted by the Act. One THE request prompted this round-robin message from Staffordshire University to other FoI officers: “I have been asked by my student office if we can refuse this using this [commercial interests] exemption, I can’t see how we could – any thoughts?”

With the government pushing the idea of greater competition between institutions, we can expect this exemption to be wheeled out more frequently in the future, Gibbons predicts. “That’s something that may well happen because the government’s got this agenda to make us more commercial.”
…
Should universities – as free-thinking and occasionally contrarian bodies – pay more heed to the current vogue for transparency? Transparency is fine, says Thomas Docherty, professor of English and comparative literature at the University of Warwick, “as long as it is not confused with truth or even reliable knowledge. Knowledge of what is going on in any institution is not revealed by raw data.

“The demand for transparent data to operate as a kind of substitute for knowledge or truth is part of a culture of ‘immediacy’,” he thinks, which is “anathema to knowledge, and to education, both of which require time, delay, and the mediations of thinking.”

I’ve done a little digging before around university funding (e.g. and First Dabblings with the Gateway to Research API Using OpenRefine), though as mentioned in Public Sector Transparency – Do We Need Open Receipts Data as Well as Open Spending Data?, we could gain even more “transparency” if public bodies also started to publishing receipts data.

University business can also leak out in to other domains… for example, looking through OpenCorporates last night, I noticed that some directors had been added to the FutureLearn listing. My first thought was to see whether any pairing (or larger grouping) of FutureLearn directors were also co-directors of any other companies. Here’s what I found:

(We can also look up to see what other companies currently have directors with the same names as directors of the companies listed above (here). Note that there may be some ambiguity here – exact string matches on names doesn’t mean that the names refer to the same person…)

Bookhire Limited was new to me, so I tried a quick search, in turn stumbling on a not recently updated OU FOI page – Subsidiary Companies. Do other universities tend to publish such a page?

Hmmm… maybe that would be a useful thing to collect on data.ac.uk?

PS as well as looking up university registered businesses via co-directorships, we might also try to track down companies based on DNS registrations. For example, here are some DNS nameservers for open.ac.uk and here are some domains registered on an OU nameserver.

A couple of months ago, I wondered whether this approach might also be used to estimate when folk started following an individual on Twitter. My reasoning went something like this:

One of the things I think is true of the Twitter API call for the followers of an account is that it returns lists of followers in reverse accession order. So the person who followed an account most recently will be at the top of the list (the first to be returned) and the person who followed first will be at the end of the list. Unfortunately, we don’t know when followers joined, so it’s hard to spot bursty growth in the number of followers of an account. However, it struck me that we may be able to get a bound on this by looking at the dates at which followers joined Twitter, along with their ‘accession order’ as followers of an account. If we get the list of followers and reverse it, and assume that this gives an ordered list of followers (with the follower that started following the longest time ago first), we can then work through this list and keep track of the oldest ‘created_at’ date seen so far. This gives us an upper bound (most recent date) for when followers that far through the list started following. (You can’t start following until you join twitter…)

So for example, if followers A, B, C, D in that accession order (ie started following target in that order) have user account creation dates 31/12/09, 1/1/09, 15/6/12, 5/5/10 then:
- A started following no earlier than 31/12/09 (because that’s when they joined Twitter and it’s the most recent creation date we’ve seen so far)
- B started following no earlier than 31/12/09 (because they started following after B)
- C started following no earlier than 15/6/12 (because that’s when they joined Twitter and it’s the most recent creation date we’ve seen so far)
- D started following no earlier than 15/6/12 (because they started following after C, which gave use the most recent creation date seen so far)

That’s probably confused you enough, so here’s a chart – accession number is along the bottom (i.e. the x-axis), joining date (in days ago) is on the y-axis:

NOTE: this diverges from the accession graph described above, where accession number goes on the y-axis and rank ordered event along the x-axis.

What the chart shows is an estimate (the red line) of how many days ago a follower with a particular accession number started to follow a particular Twitter account.

As described in Sketches Around Twitter Followers, we see a clear break at 1500 days ago when Twitter started to get popular. This approach also suggests a technique for creating “follower probes” that we can use to date a follower record: if you know which day a particular user followed a target account, you can use that follower to put a datestamp into the follower record (assuming the Twitter API returned followers in reverse accession order).

Here’s an example of the code I used based on Twitter follower data grabbed for @ChrisPincher (whose follower profile appeared to be out of sorts from the analysis sketched in Visualising Activity Around a Twitter Hashtag or Search Term Using R). I’ve corrected the x/y axis ordering so follower accession number is now the vertical, y-component.

require(ggplot2)

processUserData = function(data) {
    data$tz = as.POSIXct(data$created_at)
    data$days = as.integer(difftime(Sys.time(), data$tz, units = "days"))
    data = data[rev(rownames(data)), ]
    data$acc = 1:length(data$days)
    data$recency = cummin(data$days)

    data
}

mp_cp <- read.csv("~/code/MPs/ChrisPincher_fo_0__2013-02-16-01-29-28.csv", row.names = NULL)

ggplot(processUserData(mp_cp)) +  geom_point(aes(x = -days, y = acc), size = 0.4) + geom_point(aes(x = -recency, y = acc), col = "red", size = 1)+xlim(-2000,0)

Here’s @ChrisPincher’s chart:

The black dots reveal how many days ago a particular follower joined Twitter. The red line is the estimate of when a particular follower started following the account, estimated based on the most recently created account seen to date amongst the previously acceded followers.

We see steady growth in follower numbers to start with, and then the account appears to have been spam followed? (Can you spot when?!;-) The clumping of creation dates of accounts during the attack also suggests they were created programmatically.

[In the "next" in this series of posts [What Happened Then? Using Approximated Twitter Follower Accession to Identify Political Events], I’ll show how spikes in follower acquisition on a particular day can often be used to “detect” historical news events.]

PS after coming up with this recipe, I did a little bit of “scholarly research” and I learned that a similar approach for estimating Twitter follower acquisition times had already been described at least once, at the opening of this paper: We Know Who You Followed Last Summer: Inferring Social Link Creation Times In Twitter – “We estimate the edge creation time for any follower of a celebrity by positing that it is equal to the greatest lower bound that can be deduced from the edge orderings and follower creation times for that celebrity”.

wouldn't it be wonderful if somebody put together a Coursera course on Bitcoin, covering whole range: crypto, ops, economics, politics?

—
stefano bertolo (@sclopit) April 04, 2013

(In case that livelink dies, it’s a tweet from @sclopit: “wouldn’t it be wonderful if somebody put together a Coursera course on Bitcoin, covering whole range: crypto, ops, economics, politics?”)

Here’s a crappy graph I’ve used before…

It hints at how I see different sensemakers working together to help inform folk about how the world works… This was may how things were – maybe hard edges and labels need changing in a reinvention of how we make sense of the world and communicate it to others?

This was wrong – Publisher led mini-courses – but it still feels like a piece in a possibly new-cut jigsaw.

By chance, I also spotted this for the first time yesterday, even though it’s been around for some time: O’Reilly School of Technology. Self-paced, online courses with emailable tutor. (Similar context – The Business of HE Moves On….)

And this today: Facts Are Sacred – “A new book published by the team behind the Datablog explains how we do data journalism at the Guardian.” Books are often handy things to pin courses round, of course… (Which is to say – is there a MOOC in that?)

FutureLearn has been signing up ‘non-academic’ partners – the British Library, and the British Council, for example. I wonder if the BBC are going to join the party too? If so, then would there be a place for other publishers…?

…or does that feel wrong? Maybe the press doesn’t have the right sort of “independent voice” to deliver “academic” courses? WHich is why we maybe need to rethink the cutting of the jigsaw, or at least, a new view over it.

Who knows how the MOOC thing will play out – it reminds me in part of the educational packs companies hand out… I’m sure you know the sort of thing: Southern Water’s Waterwise packs, or ScottishPower Renewables Education Pack, Herefordshiore Council school’s waste education pack, Friends of the Earth information booklets etc etc. Propaganda? Biased to the point of distorting a “true” academic educational line? Or “legitimate” educational resources? Whatever that means? Maybe it’s more appropriate to ask if they are useful resources in the support of learning?

So are MOOCs just educational resource packs, promoting universities rather than companies or charities? But rather than catering to schools, do they maybe cater to well segmented “media consumers” looking for a new style of publication (the partwork “course”)?

And are there opportunities for media and academe to join forces producing – in quick time – long form structured pieces on the likes of, I dunno, Bitcoin, maybe, that could cover a whole range of related topics, such as in the Bitcoin case: crypto, ops, economics, politics?

Hmmm…

PS apparently FutureLearn are hiring Ruby on Rails developers (Simon Pearson/@minor9th: “On the look out for lovely Ruby on Rails devs who like working on Good Projects. FutureLearn needs you! http://www.futurelearn.com – DM me”)

This technique generalises within and across sectors, so for example we could look at how hospitals spend money with each other, or how police authorities spend money with each other. In this way, we can get a picture of how public bodies buy -and sell – services off each other. The mappings don’t have to relate to spend, either – we could equally well use this sort of model to see how hospitals transfer patients to one another, or how mental health or social care services offer out-of-area cover to each other, or how councils and housing trusts manage transfers between each other.

The insight that lets us produce this sort of view is that we have entities of a particular sort (hospitals, for example, or local councils), entering into transactions with other entities of the same sort. If these sorts of entity all operate under the same transparency rules, a requirement to publish outgoing (spend) transactions, for example, then we can recreate incoming (receipt) transactions from each entity of the same sort. For example, if local councils are required to publish details of spend over £x, then we can also learn how much councils received from other local councils by means of transactions over £x.

As the UK Government at least seems hell bent on getting markets established in the delivery of public services, markets that can include private companies, then we are faced with a possible asymmetry in transparency information.

UK Gov Policy – Making local councils more transparent and accountable to local people

The public should be able to hold local councils to account about the services they provide. To do this, people need information about what decisions local councils are taking, and how local councils are spending public money.

And from the NHS:

NHS – Transparency of Spend

As part of the government’s commitment to greater transparency, there is a requirement to publish online each NHS organisation’s expenditure over £25,000. In accordance with the requirement NHS Direct publish this on the basis of payments made in each calendar month.

For example, if hospital A buys significant services off hospital B, and must report that spend under transparency legislation, we can build up a picture not only relating to A’s spend, but also B’s sale of services, because A’s data relating to spend with B is openly available; which means B’s receipts from A are also available. (In this example, if items can be itemised as less than £25k per item, then this form of reporting under transparency guidelines is not required.)

If hospital A now buys service of company C, then we can look up spend from hospital A to get a picture of how much public money is flowing out to the private sector and into company C. That is, we can get an idea of company C’s receipts from openly published hospital spending data. (Of course, games could be played with itemisation – 10 treatments at £3k a treatment would result in a ‘must declare’ spend of £30k on the course of treatment, but an undeclarable £3k per treatment if billing is organised that way.)

But what if company C buys services off hospital B (maybe even subcontracting services it was contracted to deliver by hospital A)? If the spend data of company C is not subject to transparency requirements, and the receipt data from the hospital is not publicly available, we lose sight of how money is being spent within and across the public service.

Whilst private companies may balk at being required to publish details of their own spending data, we might still be able to recreate a picture of their spend with public services by requiring public bodies to also publish receipts data, along with the current requirement to publish spend data?