Fordism, the Language Machine revisited and Phillip Kerr’s ‘Personalized Learning and Educational Genomics’

Image: Edward Hopper. Intermission. Oil on canvas. 1963.

Three passages of possible interest:

From Huxley’s 1931 Brave New World:

Identical twins-but not in piddling twos and threes as in the old viviparous days, when an egg would sometimes accidentally divide; actually by dozens, by scores at a time.

“Scores,” the Director repeated and flung out his arms, as though he were distributing largesse. “Scores.”

But one of the students was fool enough to ask where the advantage lay.

“My good boy!” The Director wheeled sharply round on him. “Can’t you see? Can’t you see?” He raised a hand; his expression was solemn. “Bokanovsky’s Process is one of the major instruments of social stability!”

Major instruments of social stability.

Standard men and women; in uniform batches. The whole of a small factory staffed with the products of a single bokanovskified egg. “Ninety-six identical twins working ninety-six identical machines!” The voice was almost tremulous with enthusiasm. “You really know where you are. For the first time in history.” He quoted the planetary motto. “Community, Identity, Stability.” Grand words. “If we could bokanovskify indefinitely the whole problem would be solved.” Solved by standard Gammas, unvarying Deltas, uniform Epsilons. Millions of identical twins. The principle of mass production at last applied to biology.

(Huxley, 1931)

From Harris’s 1987 The Language Machine:

The equation ‘man = machine’ had long been preceded by the equation ‘machine = slave’. In Europe, that earlier equation sprang from social conditions already established in the civilisations of Greece and Rome. The social history of Western technology is largely the history of replacing slaves by machines, machines being on the whole more efficient, more docile and less expensive. When the mechanisation of cotton picking in the USA in the 1930s handed over to machines a form of labour traditionally the occupation of slaves, it merely repeated the economic lesson already taught in Gaul in the fourth century by the Roman engineer who demonstrated that a stepped series of water-powered millstones could grind more flour in a day than rotary quern grinding by slave labour could produce in a month. (It is worth noting that the slaves, both in the Graeco-Roman world and in the cotton fields of America, had originally been captives from foreign lands, and hence not members of the linguistic community.) The present-day replacement of factory works by robots continues essentially the same trend. The robots raise output, cut costs, and not go on strike; and the etymology of the word robot itself perpetuates the association between automation and slavery.

(Harris, 1987: 96)

From Philip Kerr’s recent 2018 blog post:

Cummings got his ideas from Robert Plomin , one of the world’s most cited living psychologists. Plomin, in a recent paper in Nature, ‘The New Genetics of Intelligence’ , argues that ‘intelligence is highly heritable and predicts important educational, occupational and health outcomes better than any other trait’. In an earlier paper, ‘Genetics affects choice of academic subjects as well as achievement’, Plomin and his co-authors argued that ‘choosing to do A-levels and the choice of subjects show substantial genetic influence, as does performance after two years studying the chosen subjects’. Environment matters, says Plomin , but it’s possible that genes matter more.

All of which leads us to the field known as ‘educational genomics’. In an article of breathless enthusiasm entitled ‘How genetics could help future learners unlock hidden potential’ , University of Sussex psychologist, Darya Gaysina, describes educational genomics as the use of ‘detailed information about the human genome – DNA variants – to identify their contribution to particular traits that are related to education [… ] it is thought that one day, educational genomics could enable educational organisations to create tailor-made curriculum programmes based on a pupil’s DNA profile’. It could, she writes, ‘enable schools to accommodate a variety of different learning styles – both well-worn and modern – suited to the individual needs of the learner [and] help society to take a decisive step towards the creation of an education system that plays on the advantages of genetic background. Rather than the current system, that penalises those individuals who do not fit the educational mould’.

(Kerr, July 21, 2018)

Of course someone reading this might scoff derisively that there is no real connection between the content of each of these three passages – or rather that what connection there is is one that insinuates a rather hysterical and alarmist vision of the imminent future.

But there is another sense in that what links the content of the first and the third passage is precisely that notion of the human animal as a naturally-occurring machine-like organism set out in the second.

Even without having read Gaysina’s book, there are to my mind fundamental questions here that – the quote implies – have not even been considered let alone raised and answered.

Kerr’s conclusion raises a first point, explicitly referencing Huxley’s novel of 1931:

There is much about the science that seems problematic … but it isn’t the science that concerns me most. It’s the ethics. I don’t share Gaysina’s optimism that ‘every child in the future could be given the opportunity to achieve their maximum potential’. Her utopianism is my fear of Gattaca-like dystopias … When you already have reporting of educational genomics using terms like ‘dictate’, you have to fear for the future of Gaysina’s brave new world.

But a further point is the apparent conviction that there is nothing problematic in the idea of ‘achiev[ing] their maximum potential’ – for how are we to know in advance what an individual’s maximum potential actually is?

As far as I am aware, however great the advances in predictive technologies regarding, for example, in certain meteorological and geological events, there is no science that can look at a baby in a cradle and make a confident prediction on how that child’s life will develop – except, presumably, in the most general sense of probabilities based on the lives of the existing adult population (i.e. if you are born in a developed country, your lifespan is likely to be considerably higher than if you are born in the global south; then again, this is no guarantee that a child born in the former will outlive one born in the latter).

As potential regarding human life outcomes seems to be practically infinite, it seems hard to imagine quite what Gaysin meant by this other than an extremely vague sense of ‘good’.

In other words, it seems quite impossible to determine what exactly ‘potential’ should consist of or how and when it could be recognised, and if that is the case then how would we ever know whether or not the ‘potential’ has been truly unlocked?

By a similar token, the inclusion of other factors would seem to strain the claims made for a  ‘Personalized Precision Education’ to breaking point and beyond for as according to Ben Williamson as cited by Kerr, Personalized Precision Education will also focus on

‘… the ways that individuals’ genotypes and environments interact, or how other “epigenetic” factors impact on whether and how genes become active’.

 

Even without a knowledge of the science let alone Gaysin’s evidence for the claim, skepticism does seem to be called for.

Still, I am open to be convinced if anyone can explain the issue more clearly.

via Personalized Learning and Educational Genomics

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Edward Hopper. Intermission. Oil on canvas. 1963.

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