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Here’s a beautiful description of global sensemaking in one (long) sentence:

I’m thinking of means to learn about the existence of relevant new work (alert systems), find the texts and the passages we need (search engines), find work already found by colleagues (tagging and social networking systems), find articles similar to ones we know to be relevant (recommendation systems), find articles in our own language (machine translation), navigate to cited sources (reference linking), navigate to different versions of cited sources or other relevant destinations (multiple-resolution hyperlinks), convert a text to speech when we can’t read the screen (voice readers), paraphrase articles we don’t have time to read (text summarizers), digest larger volumes of literature than we could ever read (text mining), combine independent resources to create new synergies and utility (mash-ups), find information relevant to our questions even when we don’t know the relevant keywords (semantic web), distill uncopyrightable facts from natural-language texts and enter them into queryable OA databases (knowledge extraction), pose our search queries in our own words and sometimes even get back direct answers rather than mere pointers to literature that may contain answers (natural language search engines).

That’s from Peter Suber in the SPARC Open Access Newsletter, issue #123 of July 2, 2008. (SPARC is an acronym for Scholarly Publishing and Academic Resources Coalition.)

Suber talks about the “last-mile problem for knowledge” and its two stages:

  1. getting access to texts or data
  2. getting answers to questions

I think it’s a great essay with immediate relevance to what GSm is tackling.

Read the whole article.

[tip of the hat to Bora]

Are there particular problems or issues that can be identified where the use of Sensemaking tools could make a decisive difference? I imagine this would involve a sequence of finding such a point, applying Sensemaking and related tools to surface and confirm the real nature and cause/effect sequences of what’s going on, and then visualizing/expressing the results so they can be comprehended by the public, relevant groups, and decision-makers. This is pretty much what Buckminster Fuller was suggesting through his notions of comprehensive anticipatory design science and World Game.

In my blog post Financial markets live on price-inflating bubbles? I suggest one candidate for such an acupuncture point. Hypothesis: financial markets are artificially inflating prices in bubble markets created by runaway velocity of money. If this is true the consequences are enormous. If not, let’s get this meme out of the way so we can concentrate on what’s really happening.

The GSm founding community has strong representation from people interested in the next generation of tools for structuring, manipulating and analysing dialogue and debate at different scales. Complementing these are, of course, tools that will help us work directly with the data, in order to tease out and explain hypotheses and claims that can be fed into such debates.

Gapminder is one such tool, which has been attracting a lot of attention for its compelling visualizations of time-series data.

Gapminder is a non-profit venture promoting sustainable global development and achievement of the United Nations Millennium Development Goals by increased use and understanding of statistics and other information about social, economic and environmental development at local, national and global levels. […]

…the Trendalyzer software […] unveils the beauty of statistical time series by converting boring numbers into enjoyable, animated and interactive graphics. […] Since the Trendalyzer development was taken over by Google the Gapminder Foundation maintain the same aim and uses Trendalyzer and its resources to produce videos and web service showing major global development trends with animated statistics. Such a 3 to10 minute video is called a GapCast and they are published as free web casts with the aim of promoting a fact based world view. A GapCast converts statistical time series into moving graphics in ways that allows evidence based trends to be told as simple story lines. The time series used will be made freely available in the web service called Gapminder World that enable end users to further explore the underlying statistics in Trendalyzer graphics.

Gapminder’s director Hans Roslings gives dynamic presentations with the tool, eg. at TED:

Of specific interest to this group is the application of Gapast to Climate Change: consider for instance this “Gapcast” (Gapcast #10 – Energy & emissions)

Gapminder World is all the data that they’re importing, delivered via an interactive browser app:

Gapminder World lets you explore the changing world from your own computer. Moving graphics show how the development of all countries of the world by the indicators you choose.

Gapminder World is powered by Trendalyzer and Google Spreadsheet.

The Gapminder Graphs Community is their strategy for scaling up data sharing. Since acquisition by Google in 2007, there is now the Motion Chart Google Spreadsheet Gadget to generate animated bubble graphs.

User scenario:

OK, now imagine that we can link easily into a specific point or clip in Gapminder World or Motion Chart as an evidence point, an anchor for a claim that is made within one of the other GSm discourse tools. Or that when I enter that clip in the movie player, or turn on an overlay for Community Debate: How has this data been used in arguments?, it highlights that there is apparent consensus, or a huge debate raging…

GSm architecture

The idea of creating an architecture for global sensemaking is daunting. The field is so all-encompassing, the possibilities so vast, and the wish-list so long, where do you start? In such circumstances, beginning with a few small steps to consolidate existing tools makes sense, and thinking through how that might be done may be a good way to shake out some other ideas.

Click on the diagram to see my version 0.0 thinking.

small view of Compendium map

— Andy Streich

[cross posted from Trinifar]

Over here, George Mobus poses an excellent question about the philosophical aspects of sensemaking and its operational definition. I think he provides an excellent informal definition with this:

Sensemaking … is about understanding reality sufficiently well that one feels comfortable making statements about the future. That is, not necessarily making predictions, but anticipating future possibilities based on understanding how the world works.

In that light consider this series of definitions:

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