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“The Internet is the technological basis for the organizational form of the Information Age: the network.”
This quote comes from Manual Castells (again, sorry I must be obsessed!) who with his network theory believe in technological determinism – the discussed phenomenon from the ‘unlectures’. Castells arguments are that the way we organise as people in social public networks is only possible because of the way technologies has evolved. Before networks were hierarchically controlled whereas the technologies of today allow individuals to have more power in larger networks. The whole theory about power in networks I already wrote about, so feel free to read that one also. OK back on track… So Castells also argues that no one can not not get influenced by the digital media today and that is because the technologies in our societies are the ones that set the frames around our social behavior. At least according to Castells. Niels Ole Finnemann critisises Castells. Finnemann argues that the Internet is the way it is because there is a demand in society. What he is trying to say is that the Internet does not define our society today, we define the Internet and its attributes.
It might be slightly off topic but underneath is a very good Ted Talk about how Kevin Kelly thinks technology will evolve and what technologies actually mean in our everyday lives:
This is a history about how the use of Big Data can target customers and even figure out when a teen girl is pregnant before her own father does:
Target assigns every customer a Guest ID number, tied to their credit card, name, or email address that becomes a bucket that stores a history of everything they’ve bought and any demographic information Target has collected from them or bought from other sources. A statistician named Andrew Pole employee at Target looked at historical purchasing data for all the female customers who had signed up for Target baby registries in the past and found the following:
“As Pole’s computers crawled through the data, he was able to identify about 25 products that, when analyzed together, allowed him to assign each shopper a “pregnancy prediction” score. More important, he could also estimate her due date to within a small window, so Target could send coupons timed to very specific stages of her pregnancy.”
So, Target send out coupons for baby items to female customers who achieved those ‘pregnancy scores’. One day an angry man showed up in a Target shop outside of Minneapolis, yelling at the employees for sending his daughter these coupons when she was still in high school. Well… Unfortunately the coupons weren’t send to the wrong costumer – the girl was indeed pregnant! Is this kind of Big Data use unethical when it can interfere in people’s private lives? One thing is sure, companies like Target make a lot of money of targeting their costumers with the use of Big Data.
This weeks ‘unlecture’ was about a lot of different network things and terms. What I found interesting from that ‘unlecture’ in that theatre were two things. First the term Big Data and Adrians thoughts about what happens every time we use our everyday card in supermarkets – the companies collect data but why? Another thing I found interesting was a more heavy theoretical discussion about the way we view the technological dependent society we live in today. Do our human actions create the technologies we use or do the technologies we use create the way we act as humans? Do the Internet actually dictates how we should act in our everyday lives?
This will be what this week’s posts will be about!
We keep talking about how linking to each other creates the network’s structure. Well hashtags are also a way of linking to other stuff online, and we should definitely continue using hashtags – but ONLY in writing. Imagine if we start speaking in hashtags…
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Week 9’s ‘unlecture’ was primarily about the Barabási readings for week 8, which I also wrote about yesterday. In there I wrote about my confusion about his mathematically founded theory and his mentioning of the 80/20 rule. Well I suppose that’s ok, at least Adrian shared some of my thoughts when he said: “The 80/20 rule – don’t know why that rule rules. It’s a simple model, but I don’t know where it comes from and why it isn’t 60/40”. I guess I’ll just leave it there and focus on other thoughts from the ‘unlecture’ and the Barabási readings.
The question about networks having centers, were again a part of the ‘unlecture’ today. Adrian said something interesting about how hubs are defined by how many connections they have, in and out. That made me think about how a network as LinkedIn works and how the nodes inside that networks organise themselves. If you as a node know the hubs, the people who are connected to a lot of people, you would have easier access to a greater network, than if you only know newcomers in the LinkedIn network. I don’t know if that is preferable, but I do know fellow students from Denmark who have been offered very relevant jobs because of their network on LinkedIn.
Working with the term ‘hub’ got me thinking about what Brian said today about how networks are dynamic and that they are growing. Further more he talked about how the network of cities are changing over time and therefor the hubs changes location. That makes sense and if you look at some sort of flight plan, it is probably easy to identify the cities with the most connections, in and out like with the hubs on LinkedIn. But but but.. I am just wondering. Wouldn’t the hub be different in terms of what you are looking for? What I am trying to say is; if a filmmaker is searching for some sort of a nature location the hub might appear as a small town on Iceland, and if the family are looking for the next vacation location the hub might appear as Kuta on Bali. Again on LinkedIn, a ‘hub person’ for me might not be an international well known doctor from New York, but he might be to a person studying medicine.
I’m a bit confused now…
Barabási, the Hungarian-American physicist born in Transylvania, Romania is known for his work in the research of network-theory. Barabási played a role in the discovery of the scale-free network concept, which figures in the category of statistical physics of complex systems. A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. That is, the fraction P(k) of nodes in the network having k connections to other nodes goes for large values of k as where y is a parameter whose value is typically in the range 2 < y < 3, although occasionally it may lie outside these bounds (say what!?). In the readings for week 9: “The 80/20 Rule” and “Rich get Richer” Barabási explains his network-theory and how it developed.
I can’t say I totally understand his scale-free network concept. I think it seems complex and way to mathematical for me to understand compared to Castells and his power-in-networks theory. Barabási’s theory is clearly grounded in the positive ontology and the assumption that there is an objective reality ‘out there’ that we can observe, represent and make corresponding thrush claims about. Therefor the theory’s epistemology (what knowledge is and how we can acquire it) dualistic – it’s separating mind and matter. What I am trying to say is that Barabási’s theory measure data in order to understand the way we are all connected online, which for me seems kind of cryptic. What about questions like: “Why do nodes link to preferential nodes?” and “Why do nodes add themselves to networks and make them grow?”. Aren’t nodes the same as individuals and do individuals not have different demographics and psychographics which make them act and interpret differently inside networks, like Stuart Hall would say?
I’m not saying that the theory of the scale-free network doesn’t make sense at all, I’m just saying that I think there are missing pieces in the puzzle.
“The Long Tail” was the central reading of the discussion in this weeks ‘unlecture’. The ‘panel’ discussed the recommendations hierarchy of the free-market model and how it has changed with the internet. The way taste is divided in to different networks today, when we search online is not only defined by our demographics (age, gender, etc) but also by the way we move around online by clicking, searching an so on. So network clusters form hereby but as Adrian said; different platforms have quite different recommendation hierarchies. With that being said, I’m not sure I totally understand the way this really works.
“Does a network have a center? Or do we all create centers for our own networks?” This question was also a part of this weeks ‘unlecture’. Brian don’t think so, Elliot said it depends on the subject, theme, etc. of the network. Adrian argued that networks outside of the internet do have centers, like for an example the center of the royal family in the UK would be Buckingham Palace. When it comes to networks in the internet they won’t have a center but there will be some sort of a power structure inside those. Lots of links gives power. According to Manuel Castells (2009) a network society is a society wherein the social structures are build upon networks and the links between those. Castells defines the power inside those networks as:
”Power is the relational capacity that enable a social actor to influence asymmetrically the decisions of other social actor(s) in ways that favor the empowered actor’s will, interest and values” (Castells, 2009, p. 10).
When Castells talks about power he makes it clear that power inside networks can’t be equally divided between the nodes (the different actors) and the different nodes can each possess different kinds of power. Castells argue that the nodes that possess the ability to create networks and to create relations between networks, possess the network-making power and this is where the actual power is located in the network society. As Adrian said it might not be possible to identify a center of networks but with Castells as a helping hand it is possible to identify power inside of networks. So maybe it is possible to claim that Adrian and “The Networked Media” blogsite have the actual power in the network of The Networked Media subject, because he acts like a programmer/switcher that combines the nodes in the network – the students studying Networked Media.
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