What small boats tell us about belonging

Do I belong to these politics? And do these politics belong to me?

George Pitcher

George is a visiting fellow at the London School of Economics and an Anglican priest.

A grainy surveillance picture of an rusty boat overloaded with people

A small boat overloaded with migrants.

BBC.

Our son used to say that “home is where the dogs are”, as he was greeted by them. It’s a variation on “home is where the heart is”. Either way, it means that a sense of home isn’t just about place or geography, so much as family and, relatedly, the familiar.

If home were simply an address, candidates in an election campaign wouldn’t bother knocking on doors to meet people. To be familiar is to meet people where they are, circumstantially as well as literally on their doorstep.

To date, the solution to the refugee crisis has been to “stop the boats”, as if our principal concern is with rubber dinghies. We’ve still not addressed the people in those boats; we’re not familiar with them, their circumstances and motivations.

I’d hazard a guess that a common desire among those who flee persecution and mortal danger is something else associated with familiarity – a sense of belonging.

The refugee belongs nowhere, until she or he reaches a new and safe home. Indeed, all of us know we’re home only when we’re somewhere we belong.

Somewheres are rooted in place and community; Anywheres are footloose and and educationally privileged. To which I would add the global category of migrants, who are Nowheres.

This is Refugee Week (17-23 June) and Thursday 20 June is World Refugee Day. It’s theme this year is “Our Home”, which is why I started this column on the nature of familiarity and belonging.

Out of which arise two questions: Do I belong to this country? And does this country belong to me? The first is fairly straightforward in a practical sense – I have a British passport and pay my taxes here, so yes I do. The second question is more complex, more of which in a moment.

When it comes to sovereign governments, the questions move from first to third person. Do you belong to (or in) this country and does this country belong to you? Again, the first question is about paperwork. The second, however, becomes crucially about exclusivity.

Exclusive ownership reaches its abhorrent nadir in a BBC2 documentary this week titled Dead Calm: Killing in the Med?, which provides evidence that the Greek coastguard has been employing masked vigilantes to cast adrift landed refugees, including women and children, in international waters and, in some cases, to throw migrants overboard to their deaths. A story told alongside the capsizing, through incompetence or otherwise, of the rust-tub Adriana, in which more than 600 migrants drowned a year ago.

These are matters for international law. But it shows where treating migrants like cargo, rather than people, takes us. It’s a mindset that could start with repellent (in both senses) wave machines, as considered by a former UK home secretary.

None of which arises if the criteria of belonging are applied. Former Prospect editor David Goodhart famously wrote that a key electoral demographic could be defined in Somewheres and Anywheres. Somewheres are rooted in place and community; Anywheres are footloose and and educationally privileged. To which I would add the global category of migrants, who are Nowheres (see above).

The key here is having nowhere to belong. Former PM Theresa May talked of “citizens of nowhere” in 2016, but she meant globe-trotting tax-exiles and the like. I mean Nowhere people, with nowhere to go – and it’s toxic for all of us that there are so many of them.

This is where the question “does this country belong to me?” carries so much human freight (like a small boat, as it happens).

To belong is an atavistic human need. American psychologist Abraham Maslow’s hierarchy of needs places belonging and love as principal needs in his pyramid between basic physicalities (such as safety) and self-fulfilment at the apex. “Belongingness”, a sense of home, is vital for human stability.

This is where the question “does this country belong to me?” carries so much human freight (like a small boat, as it happens). Simply to repel refugees like they’re someone else’s problem is massively to miss a point, because they’re going to carry on looking for somewhere to belong. So they’re going to keep coming.

Maslow identified religious groups as one of those offering a sense of belonging. I would guess as much as two-thirds of the congregation I’ve looked after over the past decade came to church for that sense of belonging, which we’re called to offer to the despised and marginalised as well as the Somewheres and Anywheres.

Miroslav Volf has written here that “God created the world to live in it” and therefore, I contend, belongs to it. So we’re called to “live in more homelike ways”, which I define as a sense of familiarity and belonging. That’s the theology of it.

We are now facing the politics of it. Nationalism is not enough. We need leaders who can solve this at a global level, which is both a political and a theological imperative.

Perhaps a way of reframing my questions, in this Refugee Week as we ponder how to vote, is: “Do I belong to these politics? And do these politics belong to me?”

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21 June 2024

4 min read

It's our mistakes that make us human

What we learn distinguishes us from tech.

Silvianne Aspray

Silvianne Aspray is a theologian and postdoctoral fellow at the University of Cambridge.

A man staring at a laptop grimmaces and holds his hands to his head.

Francisco De Legarreta C. on Unsplash.

The distinction between technology and human beings has become blurry: AI seems to be able to listen, answer our questions, even respond to our feelings. It becomes increasingly easy to confuse machines with humans. In this situation, it is increasingly important to ask: What makes us human, in distinction from machines? There are many answers to this question, but for now I would like to focus on just one aspect of what I think is distinctively human: As human beings, we live and learn in time.

To be human means to be intrinsically temporal. We live in time and are oriented towards a future good. We are learning animals, and our learning is bound up with the taking of time. When we learn to know or to do something, we necessarily make mistakes, and we take practice. But keeping in view something we desire – a future good – we keep going.

Let’s take the example of language. We acquire language in community over time. Toddlers make all sorts of hilarious mistakes when they first try to talk, and it takes them a long time even to get single words right, let alone to try and form sentences. But they keep trying, and they eventually learn. The same goes with love: Knowing how to love our family or our neighbours near and far is not something we are good at instantly. It is not the sort of learning where you absorb a piece of information and then you ‘get’ it. No, we learn it over time, we imitate others, we practice and even when we have learned, in the abstract, what it is to be loving, we keep getting it wrong.

This, too, is part of what it means to be human: to make mistakes. Not the sort of mistakes machines make, when they classify some information wrongly, for instance, but the very human mistake of falling short of your own ideal. Of striving towards something you desire – happiness, in the broadest of terms – and yet falling short, in your actions, of that very goal. But there’s another very human thing right here: Human beings can also change. They – we – can have a change of heart, be transformed, and at some point in time, actually start to do the right thing – even against all the odds. Statistics of past behaviours, do not always correctly predict future outcomes. Part of being human means that we can be transformed.

Transformation sometimes comes suddenly, when an overwhelming, awe-inspiring experience changes somebody’s life as by a bolt of lightning. Much more commonly, though, such transformation takes time. Through taking up small practices, we can form new habits, gradually acquire virtue, and do the right thing more often than not. This is so human: We are anything but perfect. As Christians would say: We have a tendency to entangle ourselves in the mess of sin and guilt. But we also bear the image of the Holy One who made us, and by the grace and favour of that One, we are not forever stuck in the mess. We are redeemed: are given the strength to keep trying, despite the mistakes we make, and given the grace to acquire virtue and become better people over time. All of this to say that being human means to live in time, and to learn in time.

So, this is a real difference between human beings and machines: Human beings can, and do strive toward a future good.

Now compare this to the most complex of machines. We say that AI is able to “learn”. But what does it mean to learn, for AI? Machine learning is usually categorized into supervised learning, unsupervised and self-supervised learning. Supervised learning means that a model is trained for a specific task based on correctly labelled data. For instance, if a model is to predict whether a mammogram image contains a cancerous tumour, it is given many example images which are correctly classed as ‘contains cancer’ or ‘does not contain cancer’. That way, it is “taught” to recognise cancer in unlabelled mammograms. Unsupervised learning is different. Here, the system looks for patterns in the dataset it is given. It clusters and groups data without relying on predefined labels. Self-supervised learning uses both methods: Here, the system uses parts of the data itself as a kind of label – such as, for instance, predicting the upper half of an image from its lower half, or the next word in a given text. This is the predominant paradigm for how contemporary large-scale AI models “learn”.

In each case, AI’s learning is necessarily based on data sets. Learning happens with reference to pre-given data, and in that sense with reference to the past. It may look like such models can consider the future, and have future goals, but only insofar as they have picked up patterns in past data, which they use to predict future patterns – as if the future was nothing but a repetition of the past.

So this is a real difference between human beings and machines: Human beings can, and do strive toward a future good. Machines, by contrast, are always oriented towards the past of the data that was fed to them. Human beings are intrinsically temporal beings, whereas machines are defined by temporality only in a very limited sense: it takes time to upload data, and for the data to be processed, for instance. Time, for machines, is nothing but an extension of the past, whereas for human beings, it is an invitation to and the possibility for being transformed for the sake of a future good. We, human beings, are intrinsically temporal, living in time towards a future good – which machines do not.

In the face of new technologies we need a sharpened sense for the strange and awe-inspiring species that is the human race, and cultivate a new sense of wonder about humanity itself.