Since the constructivist turn in the sociology of scientific knowledge, it is no longer possible to speak about the relationship between science and politics. Whereas in the older tradition of the sociology of science, one could metaphorise the political dimension of research and the political role of scientists as an interface between two different social institutions - each with their specific norms, processes and procedures, this is hardly tenable from a perspective which stresses the constructedness of knowledge. There are several reasons for this. Political considerations have been shown to play a formative role in the production of scientific knowledge which has resulted in the notion that scientific knowledge is always political through and through. The same constructivist turn has not only recreated science as a political phenomenon, but has also redefined the political itself. Both science and politics seem to have been reconstructed as networks of power with humans and artefacts as the nodes and symbolic and material translation processes as the links between the nodes.
This does create a problem, though. It becomes necessary to analyze the co-production of science and social order(s). If everything plays around in a seamless web, how can we sensibly speak about the politics of science except in the thick description of case studies? Or does it not make sense anymore to try to make generalised statements about the politics of science? This would be rather ironic since scientific research seems to have become more controversial than ever. This was the theme of two workshops organised by the Dutch graduate school Science, Technology and Modern Culture WTMC. The first, the Summer School, was held in September 2001, the other in May this year. We wished to discuss with the PhD students how one could analyse the political roles played out by scientific experts and indeed by research itself and also how one could systematically study the influence of political processes in knowledge creation. This is the more pertinent since PhD students are increasingly confronted with situations in which they are asked to advise the public in controversies relating to new technologies and state of the art research. At least this is our experience in the Netherlands: the media and public institutes in general are quite interested in students of science, including PhD students, doing case studies on, for example, new reproductive technologies, the use of scientific expertise in parliamentary debates about drug policies, or the future of cloning humans and their tissues.
The central question around which the Summer School turned out to revolve is one of language: how can one in present-day “social studies of science speak” conceptualize the political without falling back to positions that are either implicitly or explicitly based on models of the political or of science that we have been deconstructing? We do not think that we found a solution, although several candidates did turn up. The extent of the problem was clearly demonstrated in a role playing exercise the PhD students did for a whole day. The challenge was to play out a scientific hearing to inform a jury that had to judge the credibility of the science used to back up statements about global warming put forward on the tables of political decision-makers. The jury consisted of experts from different fields. They had to write a report to their government clarifiying whether a phenomenon like global warming actually exists and what course of action the government should take in the light of these conclusions. In the course of the hearing, the dispute about whether or not global warming exists, and if so what causes it, raged between the experts from the relevant scientific fields, social movements and interested parties. The PhD students had read the documents from the (Intergovernmental Panel on Climate Change) IPCC climate conference and the statements by the different parties before the exercise. Hence, they were thoroughly familiar with the line of reasoning of the actors they had to play out. Therefore it was no big surpise that the PhD students did their job very well. It was striking, we think, how all clichÈ-models of the political dimensions of scientific research dominated the discourse. The rationalist model in which “good science” should underpin and determine the political course of action; the cynical model in which every political movement or economic actor can tailor the science to their needs and find the appropriate scientific spokes person; and the legal model in which scientific arguments are one of the many different arguments that should be weighed against other considerations.
The hearing itself can be seen as an exemplar of the latter. Given the fact that the majority of the PhD students were Dutch, it may come as no big suprise that seeking consensus was the main motive that drove the actions of the jury and the different parties alike. More surprising was that it proved very difficult for the participants to actually mobilize the insights generated by the last decades of science studies in this dispute. The approach that comes closest seems to be the co-production of knowledge model (Callon, 1999), which enables one to seamlessly include actors other than researchers and to equalize influences no matter what their motive. One pays a price for this, though: the actor-network theoretical perspective effectively represents all movements in one dimension. Therefore it makes by definition invisible analytical distinctions between different types of institutions or social domains. This is the same problem brought up by early critics of Latour that ANT effectively represents all scientists as political actors and science as politics.
The take we had on the problem in the Summerschool was that of the thought figure. We proposed to see the different models and mid-level theories about recent developments in the scientific system (mode II (Gibbons et al., 1994; Nowotny, Scott, & Gibbons, 2001); triple helix (Leydesdorff & Etzkowitz, 1998; Etzkowitz & Leydesdorff, 2000); postnormal science (Funtowicz & Ravetz, 1993; Funtowicz & Ravetz, 1999; Ravetz, 1999); strategic science (Cozzens, Healy, Rip, & Ziman, 1990; Rip, 2002); coproduced science (Callon, 1999) as thought figures of politics of science. This means that images of the interplay of science and politics were understood in two-tier fashion, as at one and the same time involving epistemic claims about natural and social realities, and as cultural goods through which institutional and actor-group identities are actively shaped in tandem with reconfigurations of institutions, networks and agency.
Earlier policy models, like the socalled linear model of innovation, Don K. Price’s A Truth speaks to power, or Robert Merton’s CUDOS norms-model all had clear boundaries between science and society and were predicated on powerful metaphors that assumed clearcut boundaries between science and society. They can be seen as the product of a post-World War II social epistemology, and once commonly accepted came to function as social facts despite an anemic picture of the states of affairs they were supposed to portray. In present-day discussions regarding the Anew production of knowledge@ or a new Asocial contract for science@ the earlier images and metaphors are being replaced by new ones, this time predicated on a social epistemology informed by globalisation and fusion of different stakeholder interests. The new models and metaphors are no less anemic than their predecessors, but given the new context they serve to reinforce and legitimate new institutional arrangements where the accent is on hybridity and porosity. The models and metaphors are nevertheless part and parcel of new forms of boundary work, this time in a co-production of A metascience, social organisation and economics of research in society at various levels (micro/meso/macro) that ought to be the units of analysis for a more reflexive mode of A new science policy studies (SPS). Since the learning lies in reflection-in-action it is not unusual to find some of the scholars in this field playing a double role, for example as participant observers and experts in research foresight, social constructive technology assessment, consensus conferences around new technologies (e.g. nanotechnology), and ethical, legal and social aspects of science (ELSA) pertaining to opportunities and threats in for example biotechnology (cloning, GMO-foods). In such processes STS-scholars may have an important role to play, generating critical science policy knowledge in the very process of advising decision-makers. Therewith we come a full circle, as we are confronted with the same types of problems faced by our colleagues in the natural and social sciences that interact with politicians in the domain of global change where climate is both research and politics.
Of course, this creates a tensions between the participant/advisory role and the reflexive/analyst role, as the role play in our Summer School demonstrated. How do actors including scholars in our field themselves solve these tensions? This is apparently a question quite relevant to understanding the politics of knowledge making, yet one that cannot be answered by the usually rather abstract studies of mode II or triple helix interactions at the systemic level. It asks for case studies, either focused on the actors involved or on the communication between the actors.
This was the theme of the second workshop on the politics of science (Workshop Heterogenous Knowledge Practices) which we organised May this year. The question we put central in the discussions with the PhD students was if a methodological focus on knowledge practices could generate new questions about the politics of knowledge that remain invisible in the studies mentioned above. Steve Epstein=s study of the invasion of lay experts in the making of knowledge about Aids was one B among others - of the inspiring cases (Epstein, 1996). Epstein convincingly shows that science studies tend to Afollow the actors@ in a very narrow way, thereby in fact reifying the boundary around science that science studies are supposed to challenge. His narrative history is a succesful attempt to lay bare the politics of knowledge by following a broader category of relevant actors. Epstein is not the first to do this (see e.g. the work of Stuart Blume (Blume, 1974; Blume & Catshoek, 2001)) but his study does represent one of the new approaches to be explicit again about the political dimension of science studies without falling back into (implicit) functionalist models.
The limitation of Epstein’s work is that it focuses on the influence of lay experts in so far as they have organised themselves as social movements. Although this itself is still a topic that needs further exploration and more (comparative) case studies, there are many instances in which politics and policy do matter without a relevant social movement that can carry the invasion of the scientific by the lay experts. For example, the shaping of much of the genomics research agenda and the funding of nanotechnology research agenda=s seems to take place without much social movement influence. It might be an interesting challenge for science and technology studies to study the politics of this type of hybrid agenda building and thereby maybe re-politicise the cultural study of knowledge practices.
Reference List 1. Blume, S. (1974). Towards a political sociology of science. New York: Free Press. 2. Blume, S., & Catshoek, G. (2001). Amsterdam: Patientenpraktijk. 3. Callon, M. (1999). The Role of Lay People in the Production and Dissemination of Scientific Knowledge. Science, Technology, and Society, 4(1), 81-94. 4. Cozzens, S. E., Healy, P., Rip, A., & Ziman, J. (1990). The research system in transition. Dordrecht: Kluwer. 5. Epstein, S. (1996). Impure Science. Aids, Activism, and the Politics of Knowledge. Berkeley/Los Angeles/London: University of California Press. 6. Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: from national systems and “Mode 2” to a triple helix of university-industry-government relations. Research Policy, 29, 109-123. 7. Funtowicz, S., & Ravetz, J. R. (1999). Post-Normal Science - an insight now maturing. Futures, 31, 641-646. 8. Funtowicz, S., & Ravetz, J. (1993). Science for the Post-Normal Age. Futures, 25, 735-755. 9. Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. London: SAGE 10. Leydesdorff, L., & Etzkowitz, H. (1998). The Triple Helix as a model for innovation studies. Science and Public Policy, 25(3), 195-203. 11. Nowotny, H., Scott, P., & Gibbons, M. (2001). Re-thinking Science: Knowledge and the Public in an Age of Uncertainty. Cambridge (UK): Polity Press. 12. Ravetz, J. (1999). What is Post-Normal Science? Futures, 31, 647-654. 13. Rip, A. (2002). Regional Innovation Systems and the Advent of Strategic Science. Journal of Technology Transfer, 27, 123-131.
Authors’ addresses: Paul Wouters: Networked Research and Digital Information (Nerdi), NIWI, The Royal Netherlands Academy of Arts and Sciences, http://www.niwi.knaw.nl/nerdi, email paul.wouters@niwi.knaw.nl Aant Elzinga: University of G–teborg, Department of History of Ideas and Theory of Science; Swedish Collegium for Advanced Study in the Social Sciences, SCASSS, Uppsala; email Aant.Elzinga@SCASSS.uu.se Annemiek Nelis: University of Amsterdam, Department of Political Science; email anelis@fmg.uva.nl
A review of Our Posthuman Future, by Francis Fukuyama (Farrar, Straus and Giroux, 2002)
Americans, it is sometimes said, have had a love-affair with science and technology for well over 200 years. Already back in the revolutionary period, the American national identity came to be associated with technical progress: Benjamin Franklin discovered electricity and was an enterprising craftsman as well as a “founding father”, and Thomas Jefferson was an architect and scientist, as well as the author of the Declaration of Independence.
Later on, as the natives were defeated and the vast open plains were cultivated, it was machinery that paved the way: the mass-produced guns used by the cavalry in the Indian wars, the railroads that made it possible to move the population across the frontier, and then, in the 20th century, it was the automobile, electrification and the computer industry that, more than anything else, have come to define Americanism. In the United States, human development came to be seen in technological terms, and, as Ronald Reagan used to say back in the 1950s when he was a television salesman for the General Electric company, “progress is our most important product.” But then, of course the sixties happened, and for a brief moment the love-affair with technology turned sour. It became socially acceptable to criticize technology, and, as in Sweden, nuclear power plants and a few other symbols of progress were challenged by the emerging environmental movement, and, in some cases, technological development was actually curtailed. A group of activists even buried a car on the first Earth Day in 1970.
But the technology lovers quickly bounced back, with new toys and new products that they could manufacture and sell. And as in earlier periods of technological development - what economic historians call “long waves” - the radical innovations of the 1970s, in particular, the personal computer and genetic engineering, have simultaneously given rise to huge industries and to enormous amounts of hype. Information technology and biotechnology are seen by many pundits as the driving forces in a new era of economic expansion, and, as in the past, the new technology is glorified throughout the American society, and, for that matter, the increasingly Americanized rest of the world.
The problem, however, is that many people, in the United States and elsewhere, simply donít like genetic engineering, or see any particular reason for its development other than corporate greed and commercial hubris. At least computers can be fun; you can play games on them. But genetic engineering isnít necessarily fun. It is more a matter of solutions looking for problems to solve. Ever since that day in 1972, when scientists managed to transfer some genetic material from one organism to another in a laboratory in California, the genetic manipulators have been looking for ways to make money out of their newly discovered techniques. And almost everywhere they have looked they have run up against opposition - from environmentalists, small farmers, the religious minded, and all those people who would simply not like to have to decide whether or not to check out the genes of their forthcoming babies. Genetic engineering has raised economic problems, environmental problems, and, of course, a range of ethical and moral problems that primarily have to do with power relations, and, more specifically, with who is to have power and control over processes of life.
Now Francis Fukuyama comes along and tells us that the real problem with genetic technology has to do with political philosophy. Like the good established American academic that he is, Fukuyama loves not only technology but he also loves the American constitution - that highly flawed document, which contains a lot of talk about human rights and human nature, but not a word about slavery. What bothers Fukuyama about genetic engineering is that all that “rights” talk simple becomes irrelevant and meaningless now that the genetic manipulators are able to change the meaning of being human. All of the other economic and environmental issues pale by comparison to this fundamental issue of “posthumanity”.
Fukuyama has made a name for himself by having big thoughts, and this time, as in his earlier books, he is both inspiring and silly in just about equal doses. The inspiring part is that he provides an interesting and well-written overview of the whole debate about the genetic determinism of human behavior, which has been raging for quite awhile. There is a basic disagreement among scientists about what role the so-called genetic code actually plays in human behavior, and Fukuyama presents the debate in a readable, if overly opinionated manner (heís on the side of the genetic determinists). He also has some thoughtful things to say about the new sorts of personality-affecting drugs - Prozac and Ritalin, in particular - and again covers a wide range of literature about their costs and benefits. Perhaps most inspiring of all is the openmindedness he shows about how to deal with the challenges of biotechnology. He rightly criticizes the fact that in the United States, as opposed to Europe, there are no proper regulatory institutions in place - neither laws, government agencies, technology assessment boards (that was closed down in 1995), ethical commissions, or even ethical rules for companies - all of which exist, in one way or another, in many, but certainly not all European countries. He also challenges what might be called the conventional wisdom in the United States, namely that policy making is best left to the private marketplace, and that consumers are the ultimate decision-makers.
But the words of inspiration tend to get cancelled out by the silliness, and, in particular, the strange idea of a universal human nature that Fukuyama would have us believe hasnít changed in any fundamental way since the time of Aristotle, the guru of all Western political philosophers. The problem with that, of course, is that Aristotle, and Thomas Jefferson, as well, for that matter - another Fukuyama hero - lived in slave societies, and their idea of human nature, among other things, didnít involve working for a living. Slaves by definition were not humans, and, with such a point of departure, their political ideas strike me as somewhat inappropriate for dealing with genetic engineering. Indeed, it seems to me that we need to think about the political aspects of biotechnology in a very different manner than Fukuyama.
The real challenge of genetic engineering is that powerful techniques for manipulating elements of living organisms are almost entirely out of public control and access. In keeping with the dominant neo-liberal belief system of our time, our politicians have given private commercial companies the right to experiment with these powerful techniques without much in the way of public oversight. Making biotechnology and the biotechnology firms publicly accountable is the task at hand, not defending some old-fashioned notion of human nature that was never particularly convincing in the first place.
The Issue Crawler project at http://issuecrawler.net is really three in one - a software, visualisation and ‘live’ social science project. Where the first, the software project, is concerned, we have built the Issue Crawler - a remote, server-side machine, operated through a desktop browser, that crawls a set of specified sites, brings back the sites’ outgoing links, looks for common outgoing links (the co-links) in up to three iterations, and delivers the co-links by domain name (e.g., greenpeace.org) and by top- and second-level domain suffix (.gov, .com, .org, .edu, and their country-specific, subdomain equivalents) to an XML file. The Issue Crawler output, the XML file, is rendered into maps, dubbed ‘issue network’ maps; they make up an issue network atlas in an archive. These maps capture the state of a network of heterogeneous actors, configuring around an issue. Finally, we are able automatically schedule regular queries on the issue networks to watch them evolve over time.
I first hired two designers, graduates of the Design Academy in Eindhoven, to do not only the look, feel and object design of the piece of software as well as the entire site, but to deal with the myriad problems of navigation and use sense. I also secured ‘proper users’ at this early stage, former students of mine from the University of Vienna, who’d already suffered through 3 of my classes and who understand the theory and method of network location and issue mapping. These folks would be the co-cartographers and the user-testers, and attend a series of four mapping workshops on the ‘Social Life of Issues’, where we would push the theory and standardise the practice. (See http://www.govcom.org/workshops.html)
The Narrative Specification defined a ‘narrative algorithm’ which crawls sites, and returns co-links. It was specified to bring back not co-sites, but co-pages. This was the first conundrum. If you generate a map of relevant pages related to an issue, and more than one of those pages come from a single organisational site, the map may look strange. What’s Greenpeace doing on the map three times? On the other hand, we are looking for the most relevant material on the web per issue. If geocities or oneworld is hosting a set of distinct sites, then we don’t want the crawler to bring back 3 geocities sites, and 4 oneworld sites, if geocities and oneworld are only hosting others - organizations divided from a mother host by a mere slash. Then you’d have quite the inaccurate picture. So the solution is two-fold. We build a ‘switch’ that allows the cartographer to ‘match pages’ or ‘match sites’, and once the network is returned to you, you may ‘edit’ it. If you’ve matched pages, you select the page of a site that appears most frequently, so there’s only one site per map, but with the most relevant page. If you match sites, you delete the double sites. For the geocities case cited above, you can check for a network in two ways (pages the first time, sites the second time, say), compare them, and be reasonably assured that eventually you have the right nodes for the map.
Building in switches - allowing one or another method to be employed by virtue of turning on and off particular settings - has been our solution to many of the other conundrums. For example, the Narrative Specification also called for the starting points to be privileged. Starting points are a set of URLs one enters initially into the software, to be crawled to bring back a network of interlinked sites. Starting points are privileged in that you find their external links, and then you crawl the starting points and the external links together to find external links anew (which results in a set of actors, the ‘pool’ or ‘population’). The next iteration of the co-link analysis returns your sample, in which you seek a ‘network’. This ‘biasing’ of starting points is one ‘trick’ to the algorithm, for it ensures that the network you capture has a semblance of the starting points you entered. It meets some expectations of the issue network seeker, whilst also producing a few new unexpected actors in the outcome. (It also assumes some sophistication in choosing the initial starting points.) The Amsterdam theorist was against this, for she believes in ‘brutal co-link’ and hard network location analysis (my phrases), while the project scientist (yours truly) believes that without privileged starting points you will get ‘issue drift’. Global civil society, we both agree, is not really made up of single-issue actors (as old social movement theory has it), but rather of a more free-floating protest network potential (to paraphrase Heidegger) that moves from issue to issue. If you do brutal co-link analysis, and only match sites, you run the risk of having the same network for every issue. In the event, we’ve built in a switch to allow both methods. Privileging starting points also allows you to find a classic social network around your starting points, i.e., the starting points plus those actors that have (linking) affiliations with at least two of the starting points.
Another example of the switch solution is the number of iterations one requires in order to find a network. By iterations, I mean the number of times a set of sites are crawled and common external links returned. It’s the network location heuristic. The minimum requirement is 2 iterations (with or without starting points privileged), so we have made this the default setting. Also the depth of the crawls of the sites was an issue solved by a setting. So on the crawler interface, there is a number of settings (privilege starting points [default=off], sampling iterations [default=2], crawl depth [default=2]). There’s also a setting called ‘use stoplist’, with the default on. This blacklist is a site/page exclusion list that excludes software download pages and the like. Some cartographers protested that you actually may wish to map this sort of ‘issue’, so we allow you to turn off the stop list. A debate continues currently on whether you should be able to view that list (yes), but also edit it and save it anew (probably not). There you get into another kind of privileges debate, i.e., whether any user or just the administrator or some kind of user in between can save a new blacklist, and then this blacklist becomes the default list, etc. The moment a specification is changed or added for political or other ‘vibe’ reasons, it reverberates across the many other pieces of the puzzle.
The original project name is Live Issue Atlas, funded by the Internet Program of the Soros Foundation, New York. In discussions between Jonathan Peizer (of Soros) and myself before the grant was allocated, we debated whether the project was primarily about making a piece of software or making an issue atlas. My response was that we would make the software and the atlas, but the ‘live’ part would have to wait another day. In my view, an atlas, or a set of maps, becomes ‘live’ when they know when to refresh themselves. They would know to refresh themselves, I believe, if the network they’re based on is hot, i.e., is increasing the frequency of its page modification behaviour, perhaps increasing the link density of its network. In order to have a network (and a map) learn about itself well enough to refresh itself, it needs to first schedule a series of refreshing crawls, and note the differences in heat over time. The hotter it is in comparison to a set of previous crawls, the more frequently it should refresh itself. If it learns, the atlas is not only live, but it’s also webby and self-reliant. It’s webby in the sense that it is responding to Web dynamics, and is responsive to web users, who would be sensing for any number of (online and offline media) reasons that an issue is heating up. If the live atlas meets that expectation for those web users, then it’s timely. (Perhaps it could be said to be performing live social science, as the first workshop brief put it.) Also, it could alert folks to particular issues heating up. Finally and perhaps most importantly, it’s self-reliant - in the sense that it maintains itself, sort of like artificial life.
Some issues emerge if you try to design this, one of the larger of which is the effects of dynamic html. My solution was to exclude those pages from the refresh analysis whose datestamp is about the same time as the crawl was performed. We shall try to make the maps learn in future (here the famous phrase of the ‘second phase’ comes to mind). For the time being we have built in a scheduler for regularly scheduled refreshes. For operators sensing a heated issue, that refresh schedule could be made shorter, what have you. Thus the atlas will not be ‘live’ in the sense above, but the sets of maps will still be able to show ‘evolution’ of an issue over time through the scheduler feature.
But refresh what? One could plug the starting points back in, and determine quite wholesale changes, potentially, or, as our solution has it, especially after a long discussion with the Oneworld programmers (Cambridge University math graduates - this came in handy), we can note the smaller changes in the network (who’s now in, who’s now out), by taking the inputs of the last iteration as the new starting points. So, we are refreshing the ‘network’ on its own terms. The starting points become a little less relevant, and thus partially address some cartographers’ concerns of bias in starting point selection, i.e., whether the ‘network’ is ultimately more a product of the starting points then web issue network dynamics.
Above, I mentioned the default number of iterations as well as the default crawl level. This brings me to the most frustrating aspect of the software, and that’s the speed at which it returns a query (and the planning, and administrator crawl cancellation moments, that will have to go into making the atlas proper). I’ll preface this by reiterating that we’re not Google, and we’re not operating a database farm. We’re operating a lonely Oneworld server, with some distance from a backbone. The maximum number of starting points that can be crawled - or is ‘spec’d to be crawled; it could be more - is 300. Say you start with 300 URLs, you set the crawler depth to 3, and iterations to 3, the crawler could be working perhaps for hours. This was first brought to my specific attention when we discussed ‘email notification’. The cartographer is notified by email when the network location crawling and co-link analysis are completed. (The cartographer also may move to his/her member’s page [renamed cartographer’s page], and note a completed crawl.) Really the only way to speed up this process is to pull a SETI, and do some distributed server computing. We are now looking into devising a piece of downloadable software - like the SETI screensaver - that has a machine, once slumbering or perhaps not slumbering at all, contact the server-side software, telling it that more bandwidth and machine power are available for crawling operations, and thereby extending a helping hand for the cause. (Ultimately such a construction may encourage the emergence of a user community.)
Moving to the thought behind node classification as well as the first visualisation scheme, some time ago we mapped the Russian HIV-AIDS network on the Web. Up until that point most of the issue network mapping work had been done from a ‘govcom.org’ perspective. That is to say, we have been looking for the composition of issue networks (and the extent of the debate on issues within them) amongst three to four leading actor types per issue - governments, companies, ngo’s and scientific institutes. Noting that an issue is occupied only by com’s and org’s indicates perhaps a budding debate, whilst one occupied by gov’s and com’s a more matured, regulatory regime, for example. Knowing its composition (who’s who) and its composition type (e.g., what we call an ‘unholy alliance’ by .gov and .com, as above) was enough to build theory, talk practice, make claims. The colleague mapping HIV-AIDs in Russia (as well as Belarus and Ukraine), however, was interested in the interplay between national and international groups, and whether the nationals defined the problem (and their audiences) in one way, and the internationals in another. She also was looking for the best-positioned international actors in the Russian network. (It turns out to be the Dutch Doctors without Borders site in Russian.) In fact, she had many new questions because she came to the problem with a new pre-classification of node types. The breakthrough came when we actually mapped the two groupings, in a two-node-type scheme. Before coming to the breakthrough, I should mention that the visualisation of the Russian HIV-AIDS map was inspired by the conversations with Oneworld programmers, and the idea that a network is refreshed by using the inputs of the last iteration as starting points. We can visualise not only the final network but also those parties included in the last iteration that did not make the final network. Thus we used a kind of Turkish eye visualisation, a circle within a circle with the bottoms of both circles meeting. This shows who’s in, and who’s just out - and perhaps obviously (only to the cartographers) - from whom those actors just outside the circle would have to receive a link in order to make it into the inner circle, and count as relevant in the issue network on the Web.
We were interested in a different node type naming convention (international and Russian), and thus only a two-colour as opposed to the 4/5 colour gov.com.org.edu.country scheme. Recall that once the crawler returns your network, you may edit your network. This page is called the ‘network tuner’, where upon tuning and saving, you render your network into an actual map. At the tuner, you may edit the URLs; you may also edit the node names. (You also can raise the authority of your network, asking for only those actors who have received 3, 4 or more inlinks from the network actors.) Now, at the network tuner, we decided that you may also edit your node types, as well as the node colours. In those fields, the gov.com.org.edu.country scheme will automatically appear as suggestions (or defaults), but you may edit them. Once edited, you save your network which could just as well be with your own node naming, node type naming, and node colouring assignations. Thus, we have a generic issue network mapping tool, with the gov.com.org.edu.country as suggested frame only.
There are more details, but allow me to conclude with map viewing. The maps arrive as SVG files, which requires an adobe plug-in, viewer 3.0. If you’ve kept the gov.com.org.edu.country scheme (because you’ve kept the defaults, saved and rendered that kind of network), you may turn links on and off, and node types and links on and off, from a straightforward gov, com, org, edu-type legend. (It wouldn’t make sense to turn off only node types and be left with just links, unless you’re an artist perhaps. Though we may wish to view collective link shapes at some point.) Intriguingly, if you have tuned and saved your network with a non-gov.com.org.edu.country scheme, your map legend will be dynamically generated, so you can turn on and off mylinks, and mynode types and mylinks. (I gest with the MyNegroponte allusion.) Here we still have to sort out the colour paletting for new and different maps so that they do not correspond to the gov.com.org.edu.country colourisation. Nevertheless, there it is - a server-side generic network location and mapping tool, based on basic scientometric analysis, with settings to convince at least a portion of the webometric community. (In the next installment, we will write of our new visualisation scheme, developed with Andrei Mogoutov of the Ecole des Mines.)
Note: For user privileges, request an account at http://www.issuecrawler.net.