Suggestions for process-oriented titles of global issue reports
EXTRACT
Given the increasingly disastrous “state of the world”, and that foreseen for the future, it is appropriate to ask whether another language might enable meaning to be carried otherwise — and potentially more imaginatively and fruitfully. There is clearly a fundamental problem with respect to the relationship between states of any kind — one which obscures consideration of the dynamics which may be vital to the essence of meaning. This is only too evident in the case of Israel-Palestine, India-Pakistan, North Korea-South Korea, and the like — as with the “two-state solutions” proposed in the first case.
. . . . . . . .
With respect to enhancing insight into the dynamic, a striking innovation in “turning statistics into knowledge” — with the slogan “unveiling the beauty of statistics for a fact based world” — has been offered by the Gapminder initiative, within the context of the OECD Global Project on Measuring the Progress of Societies. A provocatve case might however be made for re-imaging “statistics” — as exemplifying state language – with something akin to “dynastics”.
. . . . . . . .
It can be readily argued that this collection of “states” offers no indication of how they are interrelated systemically between the domains so thematically bounded. This systemic connectivity has been the primary preoccupation of the Encyclopedia of World Problems and Human Potential now accessible online.
Having had some success in domestic policy decision making, with Progressive Congress News being the final result, I thought I would see if there was anything that needed doing in the realm of foreign policy. Wikistrat, [allegedly]the world’s first Massively Multiplayer Online Consultancy (MMOC), was something that was immediately visible once I graphed my personal contacts. I wrote six posts about them as I mapped their network.
Foreign Policy Process – I graphed my contacts in the foreign policy field, I found a bunch of the top organizations and subscribed to their news feeds, and then I noticed the Wikistrat group.
Foreign Policy Organizations & Individuals – two of Wikistrat’s 156 experts were LinkedIn contacts for me. I explored the subset of members who had Twitter accounts and speculated as to what additional information could be learned about them with just social media as a starting point.
Exploring Wikistrat With Maltego – Starting with the Twitter accounts of the roughly two dozen Wikistrat members, I extracted the information from current discussions of one of the busier members, hunting for signs of issue focused communities of which the Wikistrat analysts are members. I didn’t make any great discovery, this is just an exposition on the process I used.
Wikistrat’s Analysts & Friends – I extracted the list of well connected contacts for the identifiable analyst Twitter accounts. A small connected network was revealed, but it broke down as soon as I removed the organizational role accounts that were found. This fits my expectation – Wikistrat analysts have rich interactions, but they didn’t self-organize with Twitter as a base and it seems likely they don’t participate in public theater in support of their conclusions.
Wikistrat Full Network As Of 3/30/2013 – I finally had a full Wikistrat map – the names of every member and their associated profile on the company web site. Some had LinkedIn or Twitter accounts, with the professional network being found for 40% of members and Twitter accounts being located for 20%. Overlap of LinkedIn and Twitter accounts was rare – only 2% – 3% show this pattern. I applied Named Entity Recognition to the profiles on both Wikistrat and LinkedIn. I thought I might be able to identify geographic clusters, employment clusters, or education clusters. The Wikistrat profiles are very regular in their layout, but quite resistant to the efforts of the Alchemy and OpenCalais NER products. A hand coded script with a little regex could be applied to the Wikistrat profiles, but I have not continued down that path.
Hashtags & Humans – I retrieved the most recent dozen tweets for all of the Wikistrat analysts, then extracted the hashtags they were using. I found that there were some hashtags that were congregation points, but that it was more common for there to be clusters of related tags.
Maltego provides a slider that allows four different volumes of information to be returned from a transform, their term for a query. The settings are 12, 50, 255, or 10,000. Twitter related transforms often stop at 100 entities, a limit enforced by Maltego publisher Paterva’s servers. Named Entity Recognition services are tuned for actual language and don’t perform terribly well on bodies of text with specific formats, nor were they all that useful in terms of picking out entities from tweets. Once tweets were available, hashtag extraction produced useful information, but there are performance constraints here as well.
Technical performance considerations aside, this process did reveal useful information, and some old wisdom from noted social network analyst Yoga Berra are still quite applicable today:
Ariana Huffington: “the decision-makers are not acting in the best interests of the public”
Voice Over: “socio-economic evolution out of synch with natural evolution”
Joichi Ito: “frugal engineering happens in the absence of abundance”
Many good endeavors still working in silos. Sharing and cross fertilization not there yet.
Those who have been sideline by power now have ability to by-pass power and connect to all.
Published on Apr 10, 2013
What will the world look like in 50 years? The problems facing our world are so large that they demand disruptive thinking. We don't have time to think in incremental terms. It's time to challenge the status quo, and dare to imagine what we can do.
Just over four years ago I wrote my first article about Twitter and not long after that I created my first Twitter ‘program’ – a single line unix curl command placed in cron. The command executed at the top of every hour, posting the same tweet.
Things have come a long way since then. Twitter is still king of search engine placement and it’s a requirement for issue advocacy, but as a digital commons its subject to the same troubles as Washington D.C.’s Lafayette Park, directly north of the White House. There are crowds of tourists, constant low level protests, occasional large attendance events, and some nut with a bullhorn periodically turns up and shrieks “JESUS! JESUS! JESUS!” for hours on end. There are pickpockets, purse snatchers, and street mumblers, all of whom may accost others.
LinkedIn, as a professional identity network, is more like a visit to a convention. False personas are fairly rare, generally easy to spot, and the management will eject them if they do anything out of line. There is an access gradient that allows users to show as little or as much as they wish. The site’s revenue model is based on people finding value in the contents and paying for a subscription, so any griefing gets dealt with quickly.
. . . . . . . .
I am exploring explicit hive minds like Wikistrat and OpenIDEO. I am interested in implicit and ephemeral hive minds that can be found via Twitter hashtag usage. Connected humans naturally form these “tribes of mind” – they begin when any communication at a distance is possible, and packet data to mobile devices has supercharged innovation in this space.
. . . . . . . .
There are opportunities to learn and to profit for those who understand both the human and technology factors involved in this rapidly shifting landscape. We’ve proven we can react more quickly when even a portion of a population is connected, the next puzzle is how to improve our long term decision making using a similar set of tools.
This is the 1st of a 3 part series by Silvia Garcia Alonso on P2P responses to the water and energy crisis. The text is also available in Spanish on her website.
Energy is the engine of our economy. An economy based on growth that permanently demands larger amounts of energy. During the 20th century, especially during the second half of it, economic growth has come hand in hand with the easy access to fossil fuel resources, something that at that time seemed to be virtually inexhaustible.
This is the 2nd of a 3 part series by Silvia Garcia Alonso on P2P responses to the water and energy crisis. Click here for Part 1.
We have already talked about the energy crisis and the need to achieve energy independence through self-generation and the birth of P2P energy networks. At that point we were always talking about communities or households, but the logic applies equally to the distribution and generation of energy in every single country.
The UN activated the Digital Humanitarian Network (DHN) on December 3, 2012 to carry out a rapid damage needs assessment in response to Typhoon Pablo in the Philippines. More specifically, the UN requested that Digital Humanitarians collect and geo-reference all tweets with links to pictures or video footage capturing Typhoon damage. To complete this mission, I reached out to my colleagues at CrowdCrafting. Together, we customized a microtasking app to filter, classify and geo-reference thousands of tweets. This type of rapid damage assessment request was the first of its kind, which means that setting up the appropriate workflows and technologies took a while, leaving less time for the tagging, verification and analysis of the multimedia content pointed to in the disaster tweets. Such is the nature of innovation; optimization takes place through iteration and learning.
