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Fig. 2.2.2(i): Program

With digital media and its changed balance of agency, some celebrate the coming, at last, of cultural democracy. The masses can speak, instead of just being spoken-to.¹⁴⁷ Economically, they say, freely shared social production may portend the end of the old, hierarchy-bound capitalism.¹⁴⁸ The creative commons may displace private ownership of culture and knowledge.¹⁴⁹ A thousand identities might flourish, instead of being overwritten and oppressed by programs of assimilation. And as soon as the masses are granted agency they are no longer that; their differences of lifeworld and historical experience start to come to light, then proliferate in a kaleidoscope of ceaseless fracturing. This is the structural basis for optimistic talk of multiculturalism and cosmopolitanism.¹⁵⁰

Notwithstanding the liberal enthusiasm for the politics of difference, these new media are not so innocent, because a lot of money is being made on the free labor of “users.”¹⁵¹ In the old model, media workers were paid, but in the new model only the likes of real estate developers and purveyors of hydrocarbons get to be paid. So much for the “knowledge economy.” Knowledge is becoming an anti-economy, except for the massive corporations who own the platforms for unpaid and underpaid work. Unpaid: nearly all users work without remuneration, and almost all content is user-generated. Underpaid: mostly for a pittance, you can create “apps” for Apple or Google, or music for Spotify, or videos for YouTube, or write fake reviews of Amazon products. McKenzie Wark calls these digital media, not the much-vaunted “culture industries,” but the “vulture industries.”¹⁵²

Nor are the digital media entirely new in their programmatic effects, because alongside differentiating programs, assimilating programs persist. Recent literature on digital media are inclined to notice the assimilation more than the differentiation.¹⁵³

This, then, is the shape of programs that differentiate interests in the era of digital media. The social effect of such programs is to fracture, fragment, factionalize. It means that differentiating programs have become powerfully effective forms of politics.^§AS2.2.2a Instead of mass, we have a million communication bubbles, whose members can be ruled by division. When we come to analyze these frames of interest in terms of their sociability, their scope is narrow.

Fig. 2.2.2(ii) is a rough map of where we stand, a matrix of rhetorics and programs designed to advance interests, with some media examples.

Fig. 2.2.2(ii): Rhetorics and programs

What makes this shift in the dominant modes of the expression of interest from massifying to differentiating programs possible?

It is the emergence, we would argue, of a new mode of production of meaning, driven by a mix of interlingua in the form of ontologies like Margaret Masterman’s^§AS2.2.1c and technologies of multimodal computational analysis like Robert Mercer’s.^§AS2.2.1a

The interlingua part is the collection of structured data – in social media or smartphone apps, for instance,^§AS2.2.2a or online shopping data, or cookies that record web movements. It takes the form of markup and tagging whose semantics is now universal and interoperable across platforms – linking to words and images concepts of person, place, purchase, life status, or personality trait. This is captured as a myriad of instances tagged by concepts, and together these add up to finely grained “intersectional” demographics. This interlingua part, we contend, is the most powerful aspect of these technologies.

Together, this makes for a new kind of universal, multimodal meaning schema structured by underlying semantics, where the surface forms of different natural languages have become for the practical purposes of meaning exchange, surmountable, and so merely circumstantial. Underlying an ATM which offers alternative language options, for instance, is the translingual ontology of banking.^§MS3.3b When everything that comes up on the screen of a digital device is machine-translatable, its language becomes less important than its semantics, and if the text has structural and semantic markup, the translation works better.^§AS2.2.1d

Then, on top of this, and in close relation to the structured data, is machine reading of unstructured data, running over natural language with computational linguistics, and pictures with image-recognition software. The ontologies or interlingua train the machine to see patterns in the unstructured data. The algorithms can’t mean much unless they are connected to semantics, the most important part of which is to tie instances to concepts whose meanings have been standardized on the web.^§MS3.4b

Yehoshua Bar-Hillel gave up on computational linguistics when he came to the realization that “the box was in the pen”^§AS2.2b would need more than a kernel of syntax plus vocabularies for computer applications to human meaning to work. It would, he said, need a “universal encyclopedia.” For practical purposes, given the capacities of computers at the turn of the 1960s, he considered this impossible, at least in his foreseeable future.¹⁵⁴

Today we spend our lives incrementally adding data to just such a universal encyclopedia, governed by a ramshackle and overlapping interlingua of ontologies, but where nevertheless there is also a great deal of singular and now mostly universal conceptual agreement about meanings and their structures.^§MS3.4a

Facebook, Google, Amazon, Apple, and the other big companies maintain the encyclopedia. They talk to each other about overall meanings of their conceptualizations in not-for-profit, meaning-mediating entities that create the standard frameworks for digital meaning. The interlingua they create hold the whole thing together. This is the way in which the sociability of the web, and our meanings, are governed today.

Every little instantiation we make – a post, a purchase, a geotagged move, an affinity-connection to an online group, a tagged person, or a connection with an object in the internet of things, goes into this great encyclopedia, this record of meaningful human experience. Everything we do involves a play of the theoretical (ontological/conceptual/the database fields/the markup tags) and the empirical (the material/the instance/the entries in the fields/the digital objects of markup).

So, the world of ephemeral experience is now incidentally entered into the historical record and chronicled in this universal encyclopedia, including things so trivial that we have barely paid any attention to their recording: text messages and emails that might have in the past been unrecorded phone calls; web navigation paths tracked with cookies and clickstreams leading to purchases that might have in the past been untracked trips to a mall; social media likes and friend networks. These record moments of multimodal meaning that in past ages would have disappeared without trace into the ether of private forgetfulness.

The encyclopedia of digital modernity, although structured around universal ontologies and connected via interlingua, captures configurations of instances that are infinitely varied. What springs to view in any analysis of the recordings is that no two people have the same shopping habits, web-defined interests, or conversational flows in texts and emails.

The paradoxical consequence of this universal encyclopedic record is the knowability of our interests in finely differentiated detail. This was not possible in the past. With this resource, the most effective rhetorics of digital meaning will now be based in programs of differentiation. Meanings that anticipate assimilation to a singular meaning have become clumsy, heavy-handed, and anachronistic.

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¹⁴² Henry Ford, 1922, My Life and Work, Sydney, Australia: Angus & Robertson, p. 72.

¹⁴³ Joseph A. Schumpeter, 1950 [1976], Capitalism, Socialism and Democracy, New York: Harper and Row, p. 81.

¹⁴⁴ Barney Warf and Thomas Champman, 2006, “Cathedrals of Consumption: A Political Phenomenology of Wal-Mart,” pp. 163–78 in Wal-Mart World: The World’s Biggest Corporation in the Global Economy, edited by S.D. Brunn, New York: Routledge, p. 169.

¹⁴⁵ Bill Cope and Robert Black, 2001, “Print Technology in Transition,” pp. 151–71 in Creator to Consumer in a Digital Age: Book Production in Transition, Volume 1, edited by B. Cope and D. Mason, Melbourne: Common Ground.

¹⁴⁶ B. Joseph Pine, 1999, Mass Customization: The New Frontier in Business Competition, Brighton, MA: Harvard Business School Press.

¹⁴⁷ Lawrence Lessig, 2008, Remix: Making Art and Commerce Thrive in the Hybrid Economy, New York: Penguin Press.

¹⁴⁸ Yochai Benkler, 2006, The Wealth of Networks: How Social Production Transforms Markets and Freedom, New Haven, CT: Yale University Press; Lawrence Lessig, 2004, Free Culture, New York: Penguin Press.

¹⁴⁹ Lawrence Lessig, 2001, The Future of Ideas: The Fate of the Commons in a Connected World, New York: Random House; Phillip Kalantzis-Cope, 2017, The Work and Play of the Mind in the Information Age: Whose Property?, Cham, Switzerland: Palgrave/Springer.

¹⁵⁰ Charles Taylor, 1994, Multiculturalism: Examining the Politics of Recognition, Princeton, NJ: Princeton University Press; Bill Cope and Mary Kalantzis, 1997b, Productive Diversity, Sydney: Pluto Press.

¹⁵¹ Phillip Kalantzis-Cope, 2016a, “Whose Data? Problematizing the ‘Gift’ of Social Labour,” Global Media and Communication 12(3):295–309.

¹⁵² McKenzie Wark, 2012, Telesthesia: Communication, Culture and Class, Cambridge: Polity, p. 208.

¹⁵³ John Naughton, 2018, “The New Surveillance Capitalism,” Prospect, January 19; Mary Aiken, 2016, The Cyber Effect, New York: Speigel & Crau; Alexander Klimburg, 2017, The Darkening Web: The War for Cyberspace, New York: Penguin Press; Cathy O’Neil, 2016, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, New York: Broadway Books.

¹⁵⁴ Yehoshua Bar-Hillel, 1960, “The Present Status of Automatic Translation of Languages,” Advances in Computers 1:91–163, p. 160.

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Kalantzis, Mary, and Bill Cope. 2020. Adding Sense: Context and Interest in a Grammar of Multimodal Meaning. Cambridge UK: Cambridge University Press. pp. 229–235. https://doi.org/10.1017/9781108862059

Differentiation §AS2.2.2