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SCoPE Webinar Series on Emergence: Nov. 2013
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Affordances and Political Ecology
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Text Eagles and Crowd-Sourcing in the Knowledge-Economy
The transcriptions of distributed cognition and commerce
Learning and affordances are embedded in context, and in learning and social ecologies. The case of txteagles, in East Africa, provides an interesting case study of how these ecologies develop and work in practice, and what the systemic issues are that arise from these changes. Here are some initial thoughts (see also
Social, economic and communications ecologies continue to change rapidly, as more and more services, people, and in short ‘agents’, animate and inanimate become part of global digital networks, and reconfigure them in the process.
What we need is a sound analytical framework for effective action, as well as name, a model, a metaphor, or what in complexity theory could be called a
or two to anchor the debate.
George Siemens and Stephen Downes have advocated the term
as a candidate for the conceptual attractor role, and recently hosted a
on this topic, with a sequel scheduled for 2009. Bruno Latour takes a slightly different line, advocating the concept of
within in actor-network theory, which emphasises the need for an inclusive concept, including both animate and inanimate
as well as the continuous process of dis-assembly and re-assembly of the social: i.e. continuous
rather than connections
which one might (erroneously) infer from the term
which is actually much broader than that, and incorporates many aspects of networks.
The case of
(Ananthaswamy 2009: 20-21) is instructive. It has a simple and effective business model, which “allows rural Kenyans to earn airtime and money by performing small tasks such as translation and transcription using their cellphones”. Txteagle develops audio interfaces for cellphones (or mobile phones) in local dialects by asking local people to translate short phrases and “if a high percentage of people return the same answer, it is accepted by the system … the service rewards those who are correct more often than not by paying them at a higher rate.” This is also applied to the translation of longer texts, as well as for training speech-recognition engines, which has commercial and surveillance applications.
Payment is in airtime, credited to the user’s mobile phone, and one company set up a facility for airtime to be used as currency, by paying contributors in airtime, and then letting them pay for goods at the market place by transferring airtime from their phone to the seller’s mobile phone. This developed into a mobile-2-mobile (M2M?) money transfer facility, with the option of cashing in the credit at post offices. The mobile company, Safaricom, “became one of the largest banks in east Africa, and txteagle was then able to pay contributors in ‘cash’ or airtime.
Other similar facilities that have been set up include Mechanical Turk (Amazon), Wattzon and Wesabe.
In these developments mobile phones become virtual ATMs, virtual hotdesks for the development of audio interfaces, surveillance tools. translation and language services, para-currencies like airtime, and virtual banking. Mobile users become users and suppliers of services –
sumers, just as Green houses, which generate more energy than they consume, become users and suppliers of energy.
A number of interesting things are happening here, creating an ecology of networked affordances, or meta-networks, including ecologies of:
access, use and supply unique contextual linguistic knowledge.
in the network aggregate and normalise linguistic bytes, and re-configure the relative value of linguistic items and mobile actors, interfacing and transcribing knowledge, service credit and commercial credit into a meta-network which is a knowledge-communications-economy.
, as actors, gather speech-recognition ‘knowledge’ which provides transparency, and speech to text transcription, which links into commercial and surveillance functions and services. This raises a number of ethical issues of course, from the legal issues of the level of contractual non/disclosure by the speech recognition company to the mobile supplier of speech recognition bytes. Do the mobile users who are selling speech recognition data know who and where that their data is being sold on to, and whether this will impact on their linguistic community: are they contributing to their own surveillance? Probably yes.
Digital ecologies of networks
The digitalisation of credit (in money), communication (in binary-coded networks) and knowledge (in ASCII, XML and related codes) intersects in the mobile phone which becomes an impressive node and multi-transcription device, seamlessly integrating bytes of knowledge, communication and credit.
There are a range of actors and nodes within this knowledge-communications-credit-surveillance ecology: mobile phone users, linguistic communities, dialect communities, commercial communications companies, surveillance organisations (in commercial and security varieties), marketplace buyers and sellers, banks (virtual and bricks and mortar, or ‘bricks and clicks’), software programmes, networks and ecologies of all of these networks, and (possibly by their absence) regulators in all the above fields.
Crowd-sourcing and Distributed Cognition
Txteagle is an interesting example of crowd-sourcing (in commerce) and of distributed cognition (in knowledge management). In political theory it also takes forward the post/ post-modern debate on distributed subjects, or dispersed subjects, as opposed to the earlier paradigms (and ideologies) of individualism.
What it adds to distributed subjects is
as a sociological and as an economic concept; in other words, as a different take on communities and communities of practice, as they now become a potential economic ‘resource’ to be exploited in the market place just like any other resource. This raises questions of ownership, control and privacy (from the individual and the community’s point of view) and regulation (politically and economically, nationally, regionally and globally).
It also raises issues specific
issues in ecological psychology, such as the interaction between mobile phone owners as part of the crowd-as-resource, and the same mobile phone owner as a self-organising element within a complex adaptive system, which self-organises not only its actions but also its identity – jointly and severally, so to speak. This raises questions about how decisions are made on a selection of competing criteria: commercial, ethical, individual, social and cultural.
To return to the question of
conceptual attractors …
We need a term that is broader than the inter-nodal
carry too much baggage from fields like telecommunications and computing.
is coming into use, as is
and although these are useful, they too reference other fields too heavily.
Complex adaptive networks
probably comes closest, but it still leaves out the organic, dynamic, self-organising
self-reproducing elements of biology that are included in the term
So the best conceptual attractor for the field is probably:
complex adaptive ecologies,
as a reasonably acceptable shorthand for it.
Ananthaswamy, Anil (2009)
Mobilising the minds of the masses
New Scientist 14-02-2009: 20-21.
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