Transcriber: Amanda Chu Reviewer: Peter van de Ven
I'm here to tell you
why I don't tell the truth about castles.
You might think it's my job.
After all, we expect professionals
to speak with authority and give us clear-cut solutions,
and that makes us very, very nervous
because there's so much we simply don't know about history.
And as a result,
a lot of things have become established in our collective memory as the truth
simply because someone said it once,
it sounded convincing,
and nobody since has stood up to say,
"Well, we don't know exactly what it was like,
but it wasn't like that."
Take Greek temples.
Everyone knows they're made of beautiful shining white marble.
We've seen them that way for centuries, from postcards to museums,
and that establishes certain seeing habits in our heads,
where we've seen them this way so much that anything different just looks wrong.
And yet today we know for a fact
that they were painted in bright garish colors;
we're just a little unclear on some of the details.
I've colored this one in myself in about five minutes of research,
so it's likely to be wrong in all the relevant places,
and it's still more correct than the white one.
So why do we continue to show them in white?
Well, there's two reasons for that:
One is that we as humans like certainty.
And so we would prefer to be absolutely certain
even if it's the absolute certainty that we are absolutely wrong
(Laughter)
than to say, "Well,
maybe it could have been approximately,
I think, something like …"
And then there's the fact
that when we're trying to establish a new truth in people's heads,
we want it to be the correct truth this time.
But even if we're not entirely clear on all the details,
that doesn't mean we can't make a statement.
If you ask me right now what time it is,
I can't tell you,
but I don't have to shrug my shoulders and just say, "I have no idea."
I know this event is on from 12:00 till 6:00,
so that eliminates half the clock right there.
We've had our first coffee break, we've not had the second,
so it's between 2:00 and 5:00.
I know there were people ahead of me, and I'm not being told I'm out of time,
so it must be around 4:30.
Is that correct?
I don't know.
It might not be the truth, but I don't have to tell you the truth.
I just have to know how correct I'm likely to be
because how correct I am can be very, very important.
Me telling you it's about 4:30 is pretty useless
if you want to know whether you can still catch your bus;
and in that case, we might have to ask more people,
we might have to fill it in with more clues and so on;
and that's science.
We ask a question,
and then we fill in the unknown and get more and more precise.
So the scientific method is pretty well-established:
You ask a question about the world around you,
you research what you already know about it,
you design an experiment to test what you don't know about it,
you gather the data, you analyze them, and you reach a conclusion;
and that conclusion could be, "I need more data."
Then you go back, design another experiment,
run it again, gather more data,
and you get more data and more data and more data,
and suddenly you're buried in data,
and you're dealing with big data,
where scientists now have this problem
that there's so many data
they can never read them all in one lifetime.
They have to find new ways to deal with that.
And then there's me.
This is me.
You can tell I'm not the kind of scientist with a lab coat,
and my data problem is slightly different.
Basically I'm dealing with one student's lab report
that they dropped on the floor,
lost half the pages and then shuffled the rest,
and there's probably a coffee stain on the relevant bit.
So what I've got is I've got half a broken castle,
slightly burned,
I've got a legal contract from 1388 that was written by a guy
who managed to spell the name "Arnold" four different ways in three pages,
I've got some rocks from the village
that may or may not have belonged to this castle,
I have got a map that was done by a guy
for whom this was a 10-minute squiggle in an eighth-year campaign,
a painting that was drawn
about 300 years after the castle burnt down,
and a book that was probably propaganda.
And of course I could go look in the archives,
I can get another archaeological excavation going, and so on,
but at some point, there's simply no way to gather more data.
And then you expect me to take that
and mash it all up into the truth about castles?
You want a reconstruction that's so realistic
it feels like you're really there,
like every little pebble in the courtyard is just right.
There's a reason that a lot of sites and museums
don't use the word "reconstruction";
instead, you find a picture,
and next to it, it has the disclaimer "Artist's impression."
And that doesn't mean they didn't do any research;
it just means they didn't document what they researched.
We don't know who they talked to, which books they read,
which conclusions they drew,
and which other theories they discarded.
Now, imagine for a moment
that we would treat a text the same way.
You go into the museum.
There's a plaque, and it says, "Author's impression."
The author thinks there might have been a castle here.
You wouldn't take that very seriously.
So why do we treat text so differently from models?
It's because we've come to a consensus on what makes a scientific text,
and it's quite simply this.
When you're writing a scientific document,
you put in footnotes,
you cite works by previous scholars,
you show your argumentation -
you simply give your document provenance -
because showing you a picture of the truth
isn't going to help you without me explaining why it's true.
The truth is, all of these are correct at the same time.
That's the truth, but it's not a very helpful truth,
because without context, data are not information.
So I'll give you a little context.
So for a little context,
this first clock shows the time in Luxembourg,
and the second one has the time in Tokyo,
the third one is one of those annoying clocks
everyone had in their kitchens about 10 years ago
that actually run counterclockwise,
and the fourth one is not a clock, it's a barometer -
you just wouldn't know that by looking at it.
So in historic research,
when we deal with images, we know what to do:
We give those provenance through metadata and paradata.
Metadata you've probably heard.
Metadata are data about the data.
You can see those when you're browsing your computer,
and you can see who made a file, when it was made,
when it was last opened, and so on.
Paradata are slightly more complex.
Paradata are data that give context for the data,
so like how they were gathered, how they were processed,
which decisions were made about them, and so on.
The metadata for this image
would be that it was taken by me on the first of June, 2017
on a Sony compact camera.
The paradata are that it was picture 111 in a series of 128
and I took it on my first research trip to this castle.
And I love to show this picture
because this picture has everything in it that is wrong with models.
You walk up the stairs in this castle, you come to the attic,
and there's a big glass box with this model sitting in it.
And what I love about it
is that there are no data attached to it whatsoever.
You don't have a scale bar.
You don't have a date it was made or who made it.
You don't have a date it's supposed to represent.
There's nothing even to say
that it's supposed to be this castle that you're standing in.
And if you're talking about decision-making processes
in the reconstruction,
if you take a closer look at that center tower there,
it becomes very, very obvious
the size of that tower was not based on an archeological excavation
or because there was a foundation there or something.
No, that's the size of the toilet paper roll they had.
(Laughter)
And so this model makes me happy
because it's everything I'm trying to avoid.
And I'm not the only person trying to avoid this kind of thing.
A lot of intelligent people are working and avoiding this.
There are some hugely complex systems these days
that go into great detail on data,
metadata, paradata, how they all relate, and so forth;
and my favorite one takes about six months to learn.
Now that's bad enough for me as a researcher,
but imagine that you, as a museum visitor,
have to go on a six-month training course
to understand what you're seeing.
So, instead, I have a system that's just good enough for me.
I simply take my model,
and I tell you which parts are true and which ones are not.
So probably true is the easiest.
That's the category of things that I think are true
because they're still there,
so that could be things like the castle ruins.
Next, pretty close to true,
we have a lot of evidence for those.
So for example,
I was saying foundations, towers on foundations -
we fill in the gaps what we have good evidence.
Third stage, extrapolation, could be true - maybe not.
That's where I'm working on secondary and tertiary data,
like the maps and images.
And then there's my favorite category -
the stuff that's not really true.
Now, these things I need to put in my model
because the model would be missing something without it.
If I didn't put these in, I would be telling you a lie,
but I have no idea what to really put in.
It's an interesting problem.
So that's things like I know the great hall had paintings on the walls,
I will never know what exactly was painted on them,
so I have to make something up,
but if I left them as a blank stone the way they are now,
that would be making a statement.
And then, of course, I need to attach my metadata and my paradata,
and tell you why it's in that category.
And finally, I need to make very, very sure
that you don't only know why it's in that category
but which part exactly I'm talking about.
If you remember that clock from earlier,
well, I can tell you for a fact that it's Friday afternoon.
I can also tell you with absolute certainty
that sometime in the last two millennia, we had a castle on this hill.
What I cannot tell you
is whether in that window, in 1548,
we had an archway and that archway had a stone
and that stone was exactly 312 millimeters wide.
It could have been 317,
but my drawing is going to say one way or the other.
And that is the really, really interesting point for future researchers
because if I've told you I have no idea what was here,
they can use that point to research, and then they can say,
"Look, we found more data, and actually you're completely wrong.
It was 483 millimeters."
And I can say, "Hooray!"
because that advances our state of collective knowledge.
So if I'm doing science properly,
I want people to be able to prove me wrong.
So that's why I'm not going to tell you the truth about castles,
and why I make it very, very clear to you
when I'm just making it up.
(Laughter)
Thank you.
(Applause)