How Travis Kelce upped his game courting Taylor Swift

Certified romantic Tory Baucum is swept off his feet by how the celebrity romance unfolded.

Tory Baucum

Tory Baucum is the director of the Benedictine Center for Family Life, Benedictine College, in Atchison, Kansas.

A montage shows Taylor swift leaning and singing into a microphone. And, Travis Kelce in his team's kit.

Swift: Ronald S Woan Wikpedia; Kelce: All Pro Reels, Flickr.

If you live on planet earth, you no doubt have heard of our now famous local love story: Kansas City Chiefs tight end player Travis Kelce is courting pop sensation Taylor Swift. One can read multiple accounts of this special love story on the Internet. (One of my favorites was written by London's The Guardian.) I don’t intend to repeat this well-known narrative. Rather, I wish to add commentary from what my wife calls a certified “Catholic Romantic”, or what my students call me, “a lover of human love.”

From the outset, please don’t get me wrong. I do not mean to canonize Taylor Swift or Travis Kelce or propose that their relationship is the ideal. I merely want to notice some very healthy things about it.

I tip my hand in the opening sentence. I describe the relationship as “courtship” not “dating.” Courtship differs from dating in terms of its intention, methods and goal. A man courts a woman whenever he pursues her seriously for a romantic relationship that is opened to the exclusiveness of marriage. The intent (serious) and goal (exclusive) determines the methods.

They met on common turf with uncommon talent. But she first made him work “for the right to party.”

After Ms. Swift declined Mr. Kelce’s unimaginative “I’m just a good ole boy” friendship bracelet, he decided to up his game – or better – run his own route. He invited Swift to return to Arrowhead Stadium to watch him “light up the stage” just as she had done three months earlier. She accepted this time. They met on common turf with uncommon talent. But she first made him work “for the right to party.”

Courtship requires work, which brings clarity to the relationship. Ends determine methods.

Another difference between courtship and dating is that it’s a family affair. Persons are more than individuals; we are social creatures who live, move and have our being in webs of relationships. We cannot know each other truly or deeply apart from those webs that create and sustain us. At the first two Chiefs games Ms. Swift attended, she was seen cheering alongside Mr. Kelce’s mom. After those central relationships have been honored, the widening circle of friends are introduced. And good friends know their role: circle the couples relationship and then face the crowd.

Kelce’s teammate Patrick Mahomes, as usual, threaded the needle, saying:

“She’s good people. Now let’s let them alone.”

What Kelce recently told reporters was refreshing. “It feels like I was on top of the world after the Super Bowl and right now I’m even more on top of the world,” he said. And when asked about having to navigate so much public interest in his relationship, he said, “You’ve got a lot of people who care about Taylor, and for good reason.” Excellent answer.

Finally, not all courtships end in marriage. And if this one doesn’t it is not a failure. If the couple loves each other well they will leave the relationship better for having known each other. Courtship is always a growth in self-knowledge by way of self-donation. They will grow as they learn to give of themselves. May they give of themselves and by so doing learn to make their love work.

As others have already said, this is the best catch of Travis Kelce’s life. And I, for one, hope he never lets her go.

This article was first published as: The Kelce Courtship of Taylor Swift, on the Benedict College web site.

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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.