Digital culture and communications

World Community Grid is a large grid computing network that uses the idle time on its volunteers’ computers to provide the necessary computing power for humanitarian research projects.  IBM, as one of World Community Grid’s partners, plays an active role in increasing the Grid’s membership base in order to increase run-time available for research projects.

The concept is quite innovative and increasing run-time requires working with partner organizations that can likewise encourage their staff/clients to contribute their computer’s idle time to World Community Grid.  IBM managers in each country work with both strong and weak ties towards this end. The strategies for getting to critical mass (Rogers 2003) apply in this process.

1. Contact is made with top level management with an interest in corporate social responsibility.
2. Negotiations are made at the lower CIO level. This is where it is important to shape their perceptions of the innovation as technically feasible and secure.
3. A marketing campaign is targeted at the organization’s employees or clients, particularly appealing to those with an affinity with one of the projects, such as fighting cancer or saving the environment.
4. Incentives for joining range from team recognition online on World Community Grid’s web site, to trophies on a Facebook application, to media attention, which is likely if the organization aligns itself with a topical research project.

The strategy outlined is a typical example, but not the only strategy employed.  Since its inception in 2004 World Community Grid has grown to 479,103 members and 1,365,679 devices resulting in a total of 275,843 years
runtime.

References:

http://www.worldcommunitygrid.org

Rogers, Everett (2003) ‘Diffusion networks’ in Cross, Rob, Andrew Parker and Lisa Sasson (2003) Networks in the knowledge economy, Oxford and New York: Oxford University Press, pp 130 – 179.

With the Federal government’s rollout of laptops and other technology
to schools, the path is open for proliferation of 21st century tools
in schools.  However, the rollout of technology and tools may not
necessary align with current practices in schools.

Actor-Network Theory (ANT) has been used to improve the relationship
between people and technology.  It provides a framework for the steps
needed to design a methodology that will allow effective introduction
and implementation of 21st century technology in schools.   An
overview of ANT can be found here.  ANT looks at the actors in a
network and the network that is built out of the relationships between
these actors.  Actors may be human or non-human. In this case, it is
the teachers, students, parents, tools, hardware, software, course
material, curriculum, suppliers, partners that make up the network.
The application of ANT is based on four processes: enrolment,
translation, inscription and evaluation.  To apply this in the
deployment of 21st century tools, we would need to look at the roles
that could be assigned to each of the actors (enrolment) and the shift
that needs to be made to allow actors to work effectively with each
other (translation).  Once aligned, the processes that need to be
carried out can be inscribed, or in this case, standardized and
documented for ease of rollout.  Finally, it must be recognized that
change in actors’ requirements, skill or features will require ongoing
evaluation of the entire system for it to continue to be relevant to all
involved.

References:

Esnault, Liliane. ‘Actor-network theory and e-learning’, Editorial
Preface. Retrieved 22 September 2009 from
http://74.125.153.132/search?q=cache:0hT0L6aYj7kJ:www.igi-pub.com/files/prefaces/ijwltt%2520preface%25202%281%29.pdf+actor-network+theoryliliane+esnault&cd=1&hl=en&ct=clnk&gl=au.

A common criticism of people actively engaged in social networking or multi-user gaming platforms by those who aren’t is that these people should “get a life.”

There are various levels of online engagement.  In a negative sense, there is addiction.  Others perceive the internet as bringing about the disintegration of family life.  Every minute spent online is a minute spent offline, away from family and friends.  And this disassociation extends beyond the home to public spaces where people can be physically present with others yet in their own private world through their mobile devices.

In reality, time spent online does not necessarily come at the expense of real world social interaction (Hampton, 2004).  Online involvement can increase sociability (Castells, 2001). Virtual communities can increase citizen participation. (McKenna and Seidman, 2005). Successful relationships move from virtual to face-to-face relationships with possibly more long-term stability (McKenna, 2002).

Add to this all those who have been socially excluded in the real world who may now be able to participate with the rest of society – people stigmatized by illness, those with disabilities such as deaf and blind people or those who are home-bound or physically remote, women not allowed to be in physical contact with men, students stigmatized as
being slow learners and others.  Is life not richer being able to engage with a wider range of people and might it not be those who are not engaged online that are missing out?

Virtual spaces are merely new places to carry out old social practices in new ways.  In both real and virtual life, there is time wasted and time spent wisely.

References:

Castells, M (2001) ‘Virtual communities or network society?’ in The Internet galaxy, Oxford: Oxford University Press, pp 116-136.

Hampton, K ‘Networked sociability online, off-line’ in Castells, M (2004) The network society: a cross-cultural perspective, Cheltenham: Edward Elgar, pp 217-232.

McKenna, K. Y. A., Green, A. S., & Gleason, M. E. J. (2002). Relationship formation on the Internet: What's the big attraction? Journal of Social Issues, 58(1), 9-31.

McKenna, K and Seidman, G (2005) ‘You, me and we: interpersonal processes in electronic groups’ in Amichai-Hamburger, Yair, The social net: understanding human behaviour in cyberspace, Oxford: Oxford University Press, pp 191 – 217.

http://www.slate.com/id/2224932/pagenum/all/#p2. Retrieved 22 September 2009

A recent tweet on Twitter caught my attention: “Progression of Twitter relationships: follow>interact>connect elsewhere>form relationship>share contact details>meet offline.”  This was followed by another tweet asking, “What comes next?” 

These can perhaps be analysed and answered by an article by McKenna (2002) which has shown that people who can better disclose their ‘true’ self to others on the Internet than in face-to-face settings will be more likely to form close relationships online, bring those virtual relationships into their ‘real’ lives and maintain these relationships such that they endure over time.

Trust and liking form the basis for forming and growing relationships. Liking or attraction results from familiarity, reciprocity of attraction and similarity, among other things.  On Twitter, these could equate to following people we know or have heard of, following people who follow us back, following people with the same interests. Trust is built through quality interaction which progresses to intimacy.  This can occur more easily online than through face-to-face encounters as disclosure is facilitated online by the following factors:

1. Anonymity or nonimity without identifiability provides a dyadic boundary outside of which there is a reduced risk that what is being disclosed will be leaked.
2. Gating features (eg, attractiveness, age, ethnicity) are minimised.  McKenna shows that in the absence of gating features, people tend to like each other better online than they do in face-to-face encounters and this liking tends to survive face-to-face encounters.

McKenna concludes that people want to make a reality out of the important aspects of their virtual lives and make them known to their social circle of friends and family.  Parties connect on other social media platforms and then move on to face-to-face encounters.  Here begins the convergence of virtual and real lives.

What next?  McKenna’s Study 2 shows a favourable outcome in the stability of relationships formed through online involvement over traditional means (though the study is limited to relationships where there is romantic involvement).  Berscheid and Reis (1998) suggest that one’s motivation to maintain long-term relationships is based on whether the relationship is able to continue to “expand the self” and that relationships often suffer because the parties involved have reached self-expansion plateaus.

References:

Berscheid, E., & Reis, H. T. (1998). Attraction and close relationships. In D. T. Gilbert & S. T. Fiske & et al. (Eds.), The handbook of social psychology, Vol 2 (4th ed., pp. 193-281). New York, NY, US: McGraw-Hill. Pages 192-210, 222-226, 230-248. (as quoted in http://www.hciresearch.info/cscw08/?q=node/32, retrieved 22 September 2009)

McKenna, K. Y. A., Green, A. S., & Gleason, M. E. J. (2002). Relationship formation on the Internet: What's the big attraction? Journal of Social Issues, 58(1), 9-31.

Education.au recently held a seminar in Sydney with the theme, ‘What does 21st century teaching look like?” Technology providers including Telstra Adobe, Cisco and Microsoft presented their views on how 21st century learning would make an impact on parents, schools, children and the community. More interesting than the panel, however, was the Twitter backchannel on #edausem which revealed some scepticism in educators on the role of industry in education. Issues that were raised include whether industry products define how 21st century learning is delivered, whether educators are consulted in the development of tools and products, whether current tools are simply retrofitted to social practice, the relevance to local communities of ‘one-size-fits-all’ global tools, whether companies push their own products to meet their needs rather than solutions that will make a difference to the needs of the community. These all resonate to the point that 21st century learning should not be about technology, but about education. Danah Boyd presents a good case against technological determinism in education as does Castells (2004) in pointing out that “…technology can only yield its promise in the framework of cultural, organizational and institutional
transformations.”

So where then does the role of industry lie? Taking note of educators’ concerns, I would suggest industry's role lies in separating the delivery of 21st century learning solutions/products from their marketing targets, breaking down barriers to entry in the application of technology across local and remote communities, promoting/developing free, open source tools, making experts within the organization accessible to schools, engaging in conversations with educators/learners to gain a better understanding of their needs for future technological development.

Any other thoughts?

References:

CASTELLS, M. (2004) Informationalism, networks and the network society: a theoretical blueprint. IN CASTELLS, M. (Ed.) The network society: a cross-cultural perspective. Northampton, MA.

Actor-network theory (ANT) was conceived in the 1980s by Michel Callon, Bruno Latour, John Law and others in the field of science and technology studies.  These theorists asserted that knowledge “may be seen as a product or an effect of a network of heterogeneous materials.” (Law 1992)  They later generalized from “knowledge” to agents, social institutions, machines and organizations.  Law distinguishes ANT from other theories by emphasizing the fact that ‘actors’ in a network are materially heterogeneous, that is, they could be humans or non-humans.  Unlike social networks which add information to the relationships of humans, ANT aims at accounting for the very essence of societies and natures.  Latour (Latour 2005) states that ANT tries to render the social world as flat as possible in order to ensure the establishment of any new link is clearly visible.  ANT does not view networks as hierarchical structures between micro- and macro-social entities, or of one entity being more powerful than another. Instead, ANT starts by looking at interactions and how they stabilize, overcome resistance and reproduce themselves to form the macro-social.  By focusing on how interactions between micro-entities create the macro-entities, ANT is able to uncover how power, size, scope or organizations are created in networks.  Furthermore, ANT does not assume that material objects or human relations are determinate such that one drives the other.  Rather it is the interactions and relations between the two that give meaning to the actor.

There have been several misconceptions and criticisms of ANT, one of which is thinking of it as a having the characteristics of a technical network.  Latour (1997) clarifies that this is not the case since technical networks are ‘intensely connected, distant, compulsory and strategically organized’.  As such, a technical network is only one of the possible final and stabilized states of ANT.  ANT, on the other hand may be local, have no compulsory paths, no strategically positioned nodes. 

Another misconception of ANT arises when it is thought of in the same sense a social network.  However, as previously shown this is not the case. Callon (1999) points out that an actor is not necessarily human and human motivation is not necessarily involved.  Since everything is in action, the actor is either an agent that indiscriminately performs or an agent with no initiative of its own, to which activity is granted by others.

References:

Law, John (1992). Notes on the theory of the actor-network: ordering, strategy and heterogeneity. Systems Practice, 5 (4), 379-393.

-excerpts from Latour, Bruno (2005) Reassembling the social: an introduction to actor-network theory. Retrieved August 17, 2009, from http://books.google.com.au/books?id=Pdr6jbCGORsC&printsec=frontcover&source=gbs_v2_summary_r&cad=0#v=onepage&q=&f=false

-excerpts from paper by Latour, Bruno (1997) On actor network theory: a few clarifications 1/2. Retrieved August 15, 2009 from http://www.nettime.org/Lists-Archives/nettime-l-9801/msg00019.html.

Callon, Michel (1999). Actor-network theory – the market test. Actor network theory and after (Sociological Review monograph series), Oxford: Blackwell, 181-195.

Network effect refers to the effect consumers have on the value of a product or service.  A corresponding increase in value of a product or service as more consumers acquire or possess it results in a positive network effect.  Value is defined in Beckstrom’s Law as the sum of the net value of each user's transaction in a network, that is, the total benefit from all transactions in a network minus the cost of those transactions.

Reed (1999), a computer scientist from MIT defines value as the ‘value of potential connectivity for transactions.’ Many kinds of value are created within networks and the network effect differs depending on the type of network.   For instance, there is the one-to-many network where information is broadcast to several people.  The value of this network is simply N, where N is the number of people who access the information.

There is the one-to-one network which connects individuals to other individuals to conduct transactions, such as email.  The value of this network can be measured using Metcalfe’s Law, where ‘the number of potential connections each of the N customers can make is (N-1), giving a total number of potential connections as N(N-1) or N2-N. Assuming each potential connection is worth as much as any other, the value to each user depends on the total size of the network, and the total value of potential connectivity scales much faster than the size of the network, proportional to N2.’  Therefore, merely interconnecting two independent networks creates value that substantially exceeds the original value of the unconnected networks.

Reed (2001) considers the many-to-many, or group-forming network which he claims to be the most valuable of all.  This is typically characterized in online communities, multi-player games and other social sites.  Reed formulated a different value measurement system to reflect how value scales exponentially with the size of the network.  Without going through the derivation, in Reed (2001), the formula Reed has arrived at is 2" where n is the number of participants.  Therefore, Reed concludes that companies that capitalize on group-forming networks will gain the strongest advantage the internet has to offer.

However, it may be worth noting once again Beckstrom’s Law on value (Hinchcliffe 2006). Taking Facebook and Twitter as examples of networks which all want to scale to massive numbers of users, and whose users are typically concerned only about how much the network is worth to them, there can be an inflection point where suddenly each new user becomes a cost for existing users.

 
References
-excerpts from Reed, D. (1999) That sneaky exponential – beyond Metcalfe’s law to the power of community building. Retrieved 18 August 2009 from  http://www.reed.com/gfn/docs/reedslaw.html.>

-excerpts from Buley, T. (2009) How to value your networks. Retrieved August 16, 2009 from http://www.forbes.com/2009/07/31/facebook-bill-gates-technology-security-defcon.html.>

-excerpts from Hinchcliffe, D (2006). Web 2.0’s real secret sauce. Retrieved August 16, 2009 from http://web2.socialcomputingjournal.com/web_20s_real_secret_sauce_network_effects.htm

The small world theory was developed by Milgram 1967 and Pool and Kochen 1978.  It arose from an experiment by Milgram to trace the number of steps in which information packets sent out to random people could get to their destination.   Milgram examined the average number of steps along the shortest route between all possible pairs of nodes in a social network.   He concluded that any two randomly-selected individuals from anywhere in the world are connected via a chain of no more than six intermediate acquaintances.

In 1998, Steven Strogatz of Cornell University and Duncan Watts of New York University took on the task of building models for the small world phenomenon.  They used mathematical simulations to show that all sorts of large networks can be traversed in a small number of steps. They looked at the likelihood of relationships being formed in the
case of a tight-knit group of people who mostly knew each other (which Watts refers to as the Caveman world) and in the case where people had random, independent relationships (Solaria world) and the possibilities in between the two worlds.  From these, they were able to create and refine a network model affirming the fact that more new relationships are likely to be formed between people who do not have much  friends in common. Strogatz and Watts then applied their network model to other types of non-social networked systems such the electric-power grid in the western United States, the collaborative relationships of professional actors (the Kevin Bacon game) and the array of brain cells in worms.

Strogatz and Watts’ work ties in with the work of sociologist, Granovetnter. Small-world networks, they found, typically have a cluster of nodes, each connected to its immediate eighbors, with a few that connect to distant nodes. It is these distant nodes (or in Granovetter’s terms, weak ties) that hold the network together.

References:

Watts, D. (1999). Networks, dynamics and the small-world phenomenon. American Journal of Sociology, 105(2), 293-527.

Watts, D. (2003). Six degrees: the science of a connected world (1st ed). New York (W. W. Norton & Company Inc).

Granovetter, M. (1973).  The strength of weak ties. American Journal of Sociology, 78(6), 1360-1380).

A social graph is a mapping of people and their connections to others, or in graphical terms, a mapping of nodes and how they are linked in a network.

Currently, social graphs exist for different social network applications and these graphs are not transferable from one application to another.  A person’s identity needs to be created in each one and often stays within that network.  If a social application were to disappear, the social graph would most likely go along with it with one’s connections accumulated during the course of participation in that network.  Rebuilding this particular social graph could be difficult, if not impossible.  For instance, if Twitter were to disappear tomorrow, how would one go about re-connecting with all those they used to follow?

To date, a universal social graph does not exist.  However, there are moves afoot towards a common, open social graph to be shared by social networking applications.  One such initiative is the DataPortability Project, whose aim is to allow individuals to use the same connections data across different social network applications.  As of May 2009, Google, Facebook, Microsoft, LinkedIn, SixApart, MySpace and Digg have joined this project.   OpenSocial, an initiative by Google, defines a common API for social applications across multiple websites with standard JavaScript and HTML.

But the move to open applications and data raises several issues that need to be resolved such as ownership, monetization, segmentation, privacy and governance.

References:

-excerpts from Iskold, Alex (2007). Social graph: concepts and issues. Retrieved August 17, 2009 from http://www.readwriteweb.com/archives/social_graph_concepts_and_issues.php.

-from MySpace officially joins the DataPortability project.  Retrieved August 16, 2009 from http://dataportability.tumblr.com/post/34138755/myspace-officially-joins-the-dataportability-project.>

A dyadic tie is a single link between two elements in a network. Dyadic ties do not define a network, as networks are defined as having a minimum of 3 nodes and 2 links between these nodes. (Van Dijk 2006) Nevertheless, as Granovetter (1973) shows, examining dyadic ties can be a useful and important tool in analyzing social networks, particularly in linking micro and macro levels of sociological theory. He classifies dyadic ties as either strong or weak and defines the strength of a tie as “a (probably linear) combination of the amount of time, emotional intensity and intimacy (mutual confiding) and the reciprocal services which characterize the tie.”  This is assumes a positive and symmetric tie.  The stronger the tie between two
individuals, the more likely they are to have common friends or members concentrated within a group and therefore, the less likely they are to form bridges to new connections.  Weak ties are more likely to link members of different small groups and therefore offer more opportunities for individuals to integrate into communities. This conclusion, however, does not take into account content, hierarchical structure, negative ties and other issues.

References:

Van Dijk, J. (2006).  The network society (2nd edition).  London (Sage)

Granovetter, M. (1973).  The strength of weak ties. American Journal
of Sociology, 78(6), 1360-1380).